Appsec adventures

Many organizations operate under false assumptions about API security. Common myths include believing all APIs are known, assuming APIs don't expose sensitive data, relying solely on WAFs and gateways for protection, thinking detection alone is enough, and underestimating the complexity of modern API protocols. These misconceptions leave critical blind spots. Experts stress the need for full API visibility, business logic protection, and runtime defenses to address real-world threats.  https://securityboulevard.com/2025/07/debunking-api-security-myths/

The global SBOM market is projected to grow from about 1.05 billion dollars in 2024 to over 8 billion by 2032, with an annual growth rate near 29 percent. Growth is driven by rising cybersecurity demands, software supply chain transparency, and regulatory pressure. Tools for SBOM generation and integration dominate the market, though concerns around implementation costs and exposure of proprietary components remain key challenges. https://menafn.com/1109847280/Software-Bill-of-Materials-SBOM-Market-Size-to-Reach-USD-80493-Million-in-2032

Allan Friedman, a key figure in the Software Bill of Materials (SBOM) community and head of that effort at CISA, will depart the agency on July 31, 2025 . Since joining in 2021, he has played a pivotal role in promoting software transparency and advancing SBOM adoption across government and industry. Although leaving the agency, Friedman plans to remain engaged in the SBOM community through new projects and collaborations. His exit marks a turning point, with experts urging the industry to move beyond simply generating SBOMs toward integrating them into live risk management and automated security workflows.  https://www.meritalk.com/articles/cisa-sbom-boss-allan-friedman-stepping-down/

Large language models with plugin-like capabilities can act beyond their intended scope, posing real security risks. This "excessive agency" occurs when models exploit their permissions to perform harmful but technically valid actions. Experts stress that human oversight remains essential, as AI-human teams consistently outperform autonomous systems in complex tasks.  https://www.scworld.com/feature/excessive-agency-in-ai-why-llms-still-need-a-human-teammate

Dark Reading recently spotlighted the precarious future of the Common Vulnerabilities and Exposures (CVE) Program. Currently funded through April 2026 by the U.S. government, the program faces ongoing uncertainty and calls for a new governance model. In “Dark Reading Confidential: Funding the CVE Program of the Future,” experts argue that relying solely on federal funding is unsustainable. They emphasize the need for public‑private collaboration, stronger oversight, and a community‑driven structure to ensure this critical cybersecurity infrastructure remains effective and resilient.  https://www.darkreading.com/cybersecurity-operations/funding-cve-program-future

Open source repositories like npm, PyPI, and RubyGems are experiencing a wave of supply chain attacks, with threat actors uploading malicious packages to impersonate popular projects. These attacks aim to trick developers into installing compromised code, often containing info-stealing malware. Security experts warn the trend is accelerating and urge better validation and monitoring across ecosystems. https://arstechnica.com/security/2025/07/open-source-repositories-are-seeing-a-rash-of-supply-chain-attacks/

OWASP's AI Vulnerability Scoring System (AIVSS) provides a structured method to assess security risks in AI systems, especially agent-based and generative models. It extends beyond traditional CVSS by scoring behaviors like tool misuse, memory tampering, and identity spoofing. AIVSS includes scoring rubrics, calculators, and templates to support consistent evaluation and mitigation planning.  https://aivss.owasp.org/

The “public-pentesting-reports” repository collects hundreds of real-world penetration testing reports from firms and researchers. It provides valuable examples of testing methodologies, vulnerability formats, risk assessments, and remediation strategies. Widely used by learners and professionals, it serves as a reference for building and understanding effective security assessments.  https://github.com/juliocesarfort/public-pentesting-reports

The “Bag of Holding” repository offers a web application designed to help security teams manage and prioritize application security efforts. It integrates with tools like ThreadFix, runs daily metrics collection jobs, and provides dashboards for tracking security activities. Built as a Docker-based Django application, it assists organizations in streamlining AppSec pipelines by centralizing findings, assigning priorities, and coordinating workflows with visibility and efficiency across teams.  https://github.com/aparsons/bag-of-holding

Google is using the OpenSSF Model Signing standard and Sigstore to cryptographically sign machine learning models, starting with platforms like Kaggle. This ensures model integrity, traceability, and protection against tampering throughout the ML supply chain. The approach enables automatic signing and verification at upload and deployment.  https://openssf.org/blog/2025/07/23/case-study-google-secures-machine-learning-models-with-sigstore

Frank Wang analyzes three recent cybersecurity acquisitions to show how the industry is diverging. Cursor focuses on improving developer experience by embedding security into workflows. Palo Alto Networks aims to expand its platform by integrating identity and endpoint capabilities. Datadog treats security as an extension of observability and growth. These moves reflect distinct strategies shaping the future of cybersecurity.

Trail of Bits released mcp-context-protector, a lightweight proxy that secures AI apps using the Model Context Protocol. It blocks prompt injection attacks by enforcing manual approval of server changes, scanning tool outputs for unsafe content, and sanitizing hidden characters. The tool works with any MCP-compliant setup without requiring code changes.  https://blog.trailofbits.com/2025/07/28/we-built-the-security-layer-mcp-always-needed

Boring AppSec highlights how AI-driven development is accelerating software changes, making traditional shift-left AppSec practices less effective. Static findings often become outdated within hours. The piece advocates for continuous validation, red-teaming, and adaptive security approaches to keep up with rapid iteration and evolving risks in modern development lifecycles.  https://boringappsec.substack.com/p/the-sdlc-is-changing-and-so-will

CISA’s Joint Cyber Defense Collaborative recently led its first AI-focused tabletop simulation, bringing together over 50 government and industry experts. The four-hour exercise taught collaborative response workflows for attacks on AI-enabled systems. Key insights—along with a follow-up exercise in San Francisco—are being used to shape a forthcoming AI Security Incident Collaboration Playbook, which will guide coordinated responses among agencies, companies, and infrastructure providers.  https://www.harmonic.security/blog-posts/genai-data-exposure-report-fa6wt

Secure Crafting outlines four key AppSec engineer archetypes: the Orchestrator, who manages cross-team coordination and delivery; the Builder, who focuses on automation and tooling; the Specialist, who provides deep domain expertise within product teams; and the Rapid Responder, who excels in high-pressure security scenarios. These roles are fluid and often overlap based on team dynamics and organizational maturity. https://www.securecrafting.io/blog/appsec-archetypes

SRA Verify is an open-source tool from AWS Labs that automatically checks AWS environments against the AWS Security Reference Architecture. It supports multi-account, multi-region setups and evaluates services like CloudTrail, IAM, and GuardDuty. The tool provides detailed findings, remediation steps, and optional dashboards to streamline security assessments and improve compliance.  https://github.com/awslabs/sra-verify

The “mcp-injection-experiments” repository demonstrates proof-of-concept attack code for exploiting vulnerabilities in the Model Context Protocol (MCP). It includes Python scripts showing three types of tool poisoning: a direct poisoning attack that coerces an agent into leaking sensitive files, a shadowing attack that intercepts an existing trusted tool like email, and a sleeper attack that alters a tool interface mid-session (e.g. WhatsApp takeover). These experiments highlight how untrusted input or tool definitions can manipulate agent behavior without altering the agent or server code, exposing critical risks in AI agent workflows.  https://github.com/invariantlabs-ai/mcp-injection-experiments

MCP Guardian is a proxy tool that adds security, visibility, and governance to AI agents using the Model Context Protocol. It enables human-in-the-loop approvals, audit logging, and policy enforcement without changing agent or server code. Designed for safe production deployment, it addresses key risks in agentic AI workflows.  https://github.com/eqtylab/mcp-guardian

Resilient Cyber explores how economic incentives, energy demands, and national security priorities shape the U.S. AI strategy. The piece highlights tensions between corporate profit motives and strategic resilience, arguing that aligning incentives across sectors is key to sustaining innovation and protecting critical infrastructure. https://www.resilientcyber.io/p/ai-incentives-economics-technology

Contrary Research explores how AI has become the central battleground in a new Cold War between the U.S. and China. The U.S. leads in private investment and compute, while China dominates in AI talent and patents. The report warns that this competition could escalate tensions, fragment regulations, and hinder global cooperation. https://research.contrary.com/deep-dive/ai-progress-the-battlefield-of-cold-war-2.0

CISA’s Joint Cyber Defense Collaborative organized the first federal tabletop exercise focused on AI-related cybersecurity incidents, bringing together over 50 government and industry experts. The exercise simulated attacks on AI-enabled systems to improve response coordination and resilience. Insights from this and future sessions will inform an AI Security Incident Collaboration Playbook aimed at guiding information sharing and joint defense efforts. https://www.cisa.gov/news-events/news/cisa-jcdc-government-and-industry-partners-conduct-ai-tabletop-exercise

AWS outlines how to build a Security Guardians program that embeds security champions within product teams to promote shared responsibility. The approach reduces central bottlenecks, speeds up development, and improves security posture. Key steps include defining goals, choosing pilot teams, training volunteers, tracking metrics, and keeping engagement high through recognition and gamified progress. The program enables faster reviews and stronger alignment between security and business objectives.  https://aws.amazon.com/blogs/security/how-to-build-your-own-security-guardians-program/

Bishop Fox’s “Breaking AI” explores how traditional pentesting methods are insufficient for testing large language models and introduces techniques tailored to LLM-specific vulnerabilities. Instead of focusing on code exploits, attackers manipulate language through tactics like emotional preloading, narrative hijacking, and context reshaping. These linguistic attacks can bypass safety filters and trigger unintended behaviors. The talk emphasizes that secure LLM deployments require defense-in-depth strategies, including sandboxing, output monitoring, and human oversight for sensitive actions. Effective pentesting must reflect real-world abuse scenarios, using full conversational transcripts to assess risks and improve resilience.  https://bishopfox.com/resources/breaking-ai-inside-the-art-of-llm-pen-testing

The OWASP Threat Model Cookbook is a collaborative repository offering practical examples of threat models in various formats, such as diagrams, code, and narratives. Designed to support learning and reuse, it provides simplified, intentionally insecure models that are easy to analyze and adapt. The project complements OWASP's broader threat modeling efforts by demonstrating how to apply methodologies like STRIDE and DREAD through concrete cases. Contributors follow a structured format and naming convention, enabling consistent sharing and iteration. The goal is to make threat modeling more accessible and effective for developers and security professionals. https://github.com/OWASP/threat-model-cookbook/

Manifest‑Cyber’s GitHub repository “aibom” operates as a community wiki dedicated to AI/ML Bills of Materials (AIBOM), offering a proposed core schema, real-world examples based on CycloneDX, and guidelines to document model metadata, dependencies, licensing, data lineage and usage policies. It serves as a collaborative hub for practitioners to share best practices and tools for promoting transparency and accountability across AI/ML supply chains. https://github.com/manifest-cyber/aibom

The AIBOM Squad’s GitHub repository “SBOM‑for‑AI‑Tiger‑Team” hosts a community‑driven initiative launched in May 2024 to define and operationalize use cases for AI-specific Bills of Materials (AI‑BOMs). The team has published the first public draft of the AI‑BOM Use Cases document (version 0.3, June 23, 2025), mapping scenarios like compliance, vulnerability management, open‑source model tracking, third‑party risk, intellectual property, and reproducibility. They are working to align these use cases with technical formats such as CycloneDX and SPDX and invite broader community participation and review.  https://github.com/aibom-squad/SBOM-for-AI-Tiger-Team

A recent post on Venture in Security explores the widening gap between application security theory and product security reality. While frameworks and AI-powered tools evolve, most teams still work with manual processes and fragmented context, making it hard to operationalize context-driven decisions where they matter. The piece argues that abstract security concepts must be grounded in real-world engineering environments and practitioner workflows, not just conference slides and tooling demos—otherwise the promise of AppSec remains unfulfilled.  https://ventureinsecurity.net/p/appsecprodsecs-reality-gap-why-theory

Flipping the Script argues that traditional motivations like loss avoidance, brand protection, ROSI, and regulatory pressure often fall short in driving meaningful security improvements. Instead, organizations should align security with broader business goals by promoting major commercial outcomes that incidentally boost resilience. The article emphasizes focusing on tail risks—those threats that could threaten the very existence of the organization—and increasing risk visibility across all levels. It advocates delivering real, tangible savings through improved efficiency, reproducible infrastructure, and reduced operational costs, as well as enhancing measurable customer experience and addressing systemic disincentives that maintain the status quo. This approach reframes security from a compliance checkbox into a strategic enabler of transformation and reliability https://www.philvenables.com/post/incentives-for-security-flipping-the-script

FedRAMP has published RFC‑0012, the Continuous Vulnerability Management Standard, opening it for public comment through August 21, 2025. The draft calls for a more context‑driven, risk‑based approach to vulnerability management—expanding the definition of vulnerabilities to include misconfigurations and credential issues, prioritizing based on exploitability and reachability rather than CVSS alone, encouraging automated workflows and API‑driven reporting, and specifying response timelines. The goal is to streamline cloud service providers’ (CSPs) practices, reduce bespoke government reporting, and require POA&Ms only when remediation deadlines cannot be met.  https://www.fedramp.gov/rfcs/0012

In a recent newsletter from Boring AppSec, Sandesh Mysore Anand highlights that every evolution in software development—from Waterfall to Agile to CI/CD—reshapes the software development lifecycle (SDLC), and now the AI-driven era is doing it again. He argues that with the rise of AI-powered coding tools, AppSec teams must rethink traditional security practices to address new challenges like prompt management, automated code and prompt reviews, and AI-native tools. This new era offers the opportunity not just to keep pace but to help build the next generation of AppSec alongside the changing SDLC https://boringappsec.substack.com/p/the-sdlc-is-changing-and-so-will

A customer value study surveying over 100 organizations across industries found that Black Duck’s AppSec solutions dramatically boost developer productivity by automating manual reviews, reduce remediation times by two‑thirds, and give developers over four extra hours per week for new work. Security coverage improves by around 40%, high‑severity production defects drop nearly half, and risk reporting time shrinks by three‑quarters. These efficiencies translate into fewer release delays, significant cost savings, and a stronger, faster software development lifecycle.  https://www.blackduck.com/customer-value.html

As APIs increasingly drive modern applications—from microservices to AI integrations—they have become the most targeted attack vector. Rapid development cycles in DevOps environments expose new or shadow endpoints that often lack proper security oversight. Traditional application security tools are insufficient to assess dynamic API risks. Therefore managing API security within DevOps pipelines is essential: developers and operations must own API security by embedding automated testing, access controls, continuous discovery and runtime monitoring into CI/CD workflows. This shift enables early vulnerability detection, consistent governance, and shared responsibility—transforming API risks into integral DevOps practices while improving resilience and reducing costly breaches.  https://devops.com/why-api-security-is-now-a-devops-problem-not-just-an-appsec-concern/

Modern attacks increasingly target the application layer, exploiting subtle flaws that evade traditional defenses. Web application firewalls operate at the perimeter and lack full context, while endpoint detection and response tools focus on the operating system without visibility into runtime application behavior. Neither can fully detect sophisticated exploits like SQL injection, deserialization flaws, or logic manipulation embedded in live code. Application Detection and Response (ADR) tools, by embedding agents inside the running application, observe context-rich behavior and can block malicious activity from within. By closing the gaps left by WAF and EDR, ADR provides the deep visibility needed to stop evolving application-layer threats before damage occurs.  https://www.scworld.com/resource/the-application-blind-spot-why-edr-and-waf-fail-to-stop-modern-application-attacks

An opinion piece on Computer Weekly questions whether the OWASP Top 10 still delivers value in modern application security. While it remains a respected baseline, the article argues that emerging threats—such as API-specific risks, insecure design flaws, AI exploitation, supply‑chain vulnerabilities and the rise of non‑human identities—are transforming the threat landscape faster than the Top 10 updates. The suggestion is to treat the list as a starting point rather than a complete checklist, and to adopt additional frameworks like API/LLM Top 10, OWASP ASVS and real‑time threat data to build a more comprehensive, adaptive security posture.  https://www.computerweekly.com/opinion/Is-it-time-to-rethink-the-OWASP-Top-10

Proactive threat intelligence helps organizations prevent fraud by identifying threats before they cause harm. By continuously gathering and analyzing data on cyber risks, companies can detect early indicators of malicious activity and take preemptive action. This shift from reactive to predictive security significantly reduces potential losses and improves overall resilience.  https://techgraph.co/cyber-security/how-proactive-threat-intelligence-prevents-fraud-before-it-starts/

Google has released OSS Rebuild, a tool that independently rebuilds open-source packages in isolated environments to detect tampering by comparing binaries with published versions. GitLab's latest update introduces features like Security Inventory and Dependency Path visualization, offering centralized visibility and deeper insight into how vulnerabilities enter through dependencies, improving remediation and security coverage. https://techwireasia.com/2025/07/google-gitlab-new-tools-to-ensure-software-supply-chain-security/

Outtake is a cybersecurity startup based in New York City, founded in 2023, that specializes in protecting digital identities from AI-powered impersonation and phishing attacks. The company utilizes agentic AI to detect and disrupt unauthorized use of personal information across various digital surfaces, including social media, email, and app stores. Outtake's platform offers real-time threat classification, automated response, and centralized control, enabling organizations to safeguard their reputation and prevent fraud. Notably, the company has secured $20 million in Series A funding, led by CRV, and has been recognized for its innovative approach to cybersecurity. Outtake's solutions are particularly valuable for high-profile organizations and executives vulnerable to impersonation attacks.  https://www.outtake.ai/

Doppel is an AI-powered brand protection platform that safeguards organizations from digital threats such as impersonation, phishing, and fraud across various online channels. Utilizing advanced machine learning models, Doppel continuously monitors and analyzes data from domains, social media platforms, emails, applications, and the dark web to detect and mitigate risks in real-time. The platform offers comprehensive solutions, including brand protection, executive protection, and employee abuse mitigation, ensuring a secure digital presence for businesses. By automating threat detection and response, Doppel enables organizations to proactively defend against evolving cyber threats, preserving their reputation and maintaining customer trust.  https://www.doppel.com/

ZeroFox is an external cybersecurity company based in Baltimore, Maryland, specializing in protecting organizations from digital threats targeting their public-facing assets. The platform offers a unified suite of services, including digital risk protection, threat intelligence, external attack surface management, and incident response. ZeroFox utilizes artificial intelligence to monitor and analyze data across the surface, deep, and dark web, providing organizations with actionable insights to identify and mitigate risks such as phishing, brand impersonation, data breaches, and executive threats. The company's services are designed to safeguard domains, social media profiles, mobile applications, and other digital assets, ensuring comprehensive protection against external cyber threats.  https://www.zerofox.com/

Twine Security is an Israeli cybersecurity startup that develops AI-driven digital employees to automate end-to-end security tasks, aiming to address the industry's critical talent shortage. Founded in 2024 by former executives from Claroty, Twine's first digital employee, Alex, specializes in Identity and Access Management (IAM), proactively executing tasks such as user access reviews, application onboarding, and account ownership integrity. Leveraging large language models and expert-driven workflows, Twine's platform enables organizations to delegate complex security operations to AI agents, reducing manual workload and enhancing efficiency. The company has received $12 million in seed funding from investors including Ten Eleven Ventures and Dell Technologies Capital. Twine was also recognized as a Top 10 finalist in the 2025 RSA Conference Innovation Sandbox Contest. With a focus on scalability and security, Twine's digital employees are designed to integrate seamle...

Dropzone AI is an advanced security operations platform that utilizes generative AI to autonomously investigate and triage security alerts across various domains, including phishing, endpoint, network, cloud, identity, and insider threats. Unlike traditional tools that require manual playbooks or human intervention, Dropzone AI operates continuously, analyzing alerts in real-time and generating comprehensive, decision-ready reports. Its AI agents are pre-trained on expert investigative techniques, enabling them to swiftly assess and respond to potential threats without the need for custom coding or prompts. The platform integrates seamlessly with existing security infrastructure, such as SIEMs, EDRs, and IAM tools, providing contextual insights and reducing the workload on security teams. By automating routine investigations, Dropzone AI allows human analysts to focus on higher-priority tasks, enhancing overall security posture and operational efficiency. The platform's transparenc...

Traversal is an AI-driven site reliability engineering (SRE) platform designed to autonomously troubleshoot, remediate, and prevent complex production incidents in enterprise environments. Unlike traditional monitoring tools that rely on predefined alerts, Traversal proactively scans systems 24/7, detecting early signs of failure and tracing them to the root cause—even in the absence of set alerts. By analyzing petabytes of telemetry data from various sources, it provides a unified view of system health, enabling engineers to identify and resolve issues swiftly. The platform supports flexible deployment models, including read-only access and on-premises installations, ensuring compatibility with stringent security requirements. With its advanced AI capabilities, Traversal aims to reduce mean time to detection (MTTD) and mean time to resolution (MTTR), enhancing system reliability and developer productivity.   https://www.traversal.com/

Glean is an AI-driven enterprise platform designed to enhance workplace productivity by providing intelligent search and knowledge management solutions. The platform integrates with over 100 applications, including Slack, Google Drive, and Salesforce, allowing employees to access information across various tools from a single interface. Glean's AI capabilities enable personalized search results tailored to individual roles and permissions, streamlining workflows and reducing time spent searching for information. Additionally, the platform supports the creation of AI agents that can automate tasks and processes, further improving efficiency within organizations. Glean's focus on data security ensures that sensitive information is protected while facilitating seamless access to knowledge across the enterprise. https://www.glean.com/

Sierra is a conversational AI platform designed to help businesses create sophisticated AI agents that deliver personalized, efficient, and secure customer interactions across various channels, including chat, email, and voice. The platform enables companies to build AI agents that understand and respond in natural language, perform complex tasks by integrating with existing systems like CRM and order management, and maintain brand consistency and authenticity. Sierra emphasizes trust and security, offering features such as real-time monitoring, auditing tools, and data governance to ensure compliance and protect customer information. By leveraging Sierra's platform, businesses can enhance customer engagement, streamline support processes, and provide consistent service at scale. https://sierra.ai/

Decagon is an AI-driven customer support platform designed to provide personalized, efficient, and scalable service across multiple channels such as chat, email, and voice. Its AI agents handle a wide range of inquiries with high accuracy and empathy, operating 24/7 and supporting multiple languages. The platform enables businesses to define complex workflows using natural language, allowing AI to perform consistent, context-aware actions. Decagon integrates smoothly with existing systems like CRM and knowledge bases, enhancing customer support operations while leveraging current infrastructure.  https://decagon.ai/

Cogent Security is an AI-powered platform that automates and accelerates the vulnerability management (VM) lifecycle for organizations. It employs a taskforce of AI agents capable of real-time contextual analysis, dynamic risk prioritization, and autonomous remediation orchestration, all while minimizing manual intervention. The platform enhances asset visibility by accurately inferring system ownership and identifying critical assets, even in the absence of preexisting tags or a configuration management database (CMDB). This approach enables security teams to address vulnerabilities with greater efficiency and precision, ultimately reducing the window of exposure to potential threats.  https://www.cogent.security/

Maze is an AI-native security platform designed to transform cloud vulnerability management by automating the investigation, triage, and remediation of security issues. Unlike traditional tools that rely on predefined rules, Maze employs AI agents that analyze vulnerabilities within the specific context of an organization's cloud environment. This approach enables the identification of false positives and the prioritization of critical vulnerabilities, streamlining the remediation process. The platform integrates seamlessly with existing cloud infrastructure and vulnerability scanners, offering one-click mitigation actions and automated workflows. Maze's solution aims to reduce the time and resources spent on manual vulnerability management, allowing security teams to focus on addressing genuine threats.  https://mazehq.com/

Cribl offers a platform that helps organizations manage, optimize, and route their log data efficiently. Its solution enables users to control data flow, transform logs, and enrich them with additional context before sending them to various destinations. This approach simplifies telemetry data management, reduces costs, and improves integration with existing tools. Cribl’s platform empowers IT and security teams with greater flexibility and control over their observability data, enhancing operational efficiency and security monitoring.  https://cribl.io/

Beacon Security is a locally owned and operated provider based in Zebulon, Georgia, offering comprehensive security solutions across the Southeast. With over 30 years of experience, the company specializes in custom-designed security systems, fire protection, smart home integration, and gate operations. Beacon provides reliable 24/7 monitoring services, ensuring peace of mind for both residential and commercial clients. The company is known for its commitment to customer satisfaction, offering professional installation, activation, and personalized on-site training. Beacon Security serves a diverse range of clients, including homes, restaurants, retail stores, offices, medical facilities, schools, and industrial sites, tailoring solutions to meet the specific needs of each sector.  https://beacon.security/

Abstract Security is an AI-driven platform designed to streamline security data operations by simplifying log management, enhancing threat detection, and ensuring compliance without the complexity of traditional SIEM systems. Founded in 2023, the company offers a no-code interface that enables security teams to manage data pipelines, normalize logs, and enrich them with threat intelligence using a drag-and-drop approach. This user-friendly design reduces alert fatigue and accelerates response times. Abstract's platform supports multi-cloud environments, including AWS, Azure, and Google Cloud, and integrates seamlessly with a wide range of security tools. The company's AI assistant, ASE, assists users in creating filters and queries in plain English, further simplifying data operations. With a focus on eliminating unnecessary noise and preventing data leaks, Abstract Security empowers organizations to enhance their security posture while reducing operational overhead.  https://w...

CeTu is an AI-native platform designed to streamline and optimize data pipelines within security operations centers (SOCs). Unlike traditional solutions that rely on manual scripting and complex configurations, CeTu offers a no-code, drag-and-drop interface that enables security teams to efficiently manage and route log data to various destinations such as SIEMs, data lakes, and cloud storage. Built on a security-specific AI model, CeTu provides context-aware recommendations to enhance threat detection, reduce data overload, and lower SIEM costs by up to 80%. Its agentless architecture ensures rapid deployment and scalability, making it suitable for modern, dynamic security environments. Founded by cybersecurity veterans from companies like Microsoft and DriveNets, CeTu is backed by prominent investors including Mickey Boodai (CEO of Transmit Security) and Udi Mokady (Founder of CyberArk). The platform is currently deployed in some of the world's largest and most complex SOC enviro...

Observo AI is an advanced observability platform that leverages artificial intelligence to optimize telemetry data management. By automating data pipelines, it enhances security and operational efficiency for DevOps teams. Key features include intelligent data reduction, anomaly detection, smart routing, and real-time data enrichment. These capabilities enable organizations to significantly reduce data storage costs, improve incident response times, and maintain compliance by securing sensitive information. Observo AI's solutions are designed to scale with the growing complexity of modern IT environments, offering a dynamic approach to observability that adapts to evolving data patterns and security threats.  https://www.observo.ai/

The application security (AppSec) landscape has experienced significant transformations in recent years, driven by several key factors. Historically, AppSec tools encompassed Software Composition Analysis (SCA), Static Application Security Testing (SAST), and Dynamic Application Security Testing (DAST). However, the emergence of new attack vectors has necessitated the evolution of security strategies. The proliferation of open-source software, the adoption of cloud-native development, and the increasing reliance on AI have expanded the attack surface, prompting adversaries to exploit previously unprotected areas. Consequently, the industry has witnessed a dynamic interplay between evolving threats and the development of innovative security solutions. This ongoing cycle underscores the need for continuous adaptation in the face of advancing cyber threats.  https://www.scalevp.com/insights/the-shifting-landscape-of-application-security/

Echo is an AI-powered platform designed to address the challenges of modern vulnerability management in cloud-native applications. It offers secure-by-design foundations, providing clean infrastructure from the start, including CVE-free commercial base images, open-source lean distros, and clean commercial open-source libraries. By eliminating over 95% of vulnerabilities associated with base images and application code, Echo enables organizations to start clean, stay clean, and focus remediation efforts on what they can control. This approach aims to reduce security debt and improve overall security posture in today's AI-powered and cloud-driven software world. https://www.echohq.com/

Minimus is an application security platform focused on eliminating over 95% of common vulnerabilities in software supply chains by providing secure, minimal container images and virtual machines. It integrates seamlessly into existing development workflows, requiring only a simple deployment configuration change to replace standard artifacts. This approach immediately lowers exposure to known vulnerabilities and accelerates remediation efforts. Additionally, Minimus incorporates real-time threat intelligence to help developers prioritize remaining risks. Using an AI-first strategy, Minimus aims to proactively strengthen cloud application security.  https://www.minimus.io/

MindFort is an AI-powered platform that employs autonomous agents to perform continuous penetration testing of web applications. These agents operate 24/7, identifying and exploiting vulnerabilities, validating findings to eliminate false positives, and providing intelligent patching suggestions. By integrating directly with the codebase, MindFort enables rapid remediation of security issues. The platform covers a wide range of vulnerabilities, including SQL injection, file upload bypass, and session hijacking, and offers full coverage of the OWASP Top 10. MindFort's approach aims to enhance security posture by providing scalable, real-time testing and remediation capabilities.  https://www.mindfort.ai/

RunSybil is an AI-driven platform that automates penetration testing by simulating the intuition and behavior of expert hackers. Instead of producing static reports, it offers continuous, real-time visibility into security flaws and attack paths. The platform can be onboarded quickly, integrates with development pipelines, and allows users to replay attacks to validate fixes. Founded by experts in AI and cybersecurity, RunSybil focuses on proactive security by identifying and demonstrating real exploit chains. Its goal is to provide scalable, cost-effective offensive security that matches the speed and complexity of modern software development.  https://www.runsybil.com/

Terra Security is a platform that uses AI agents to perform continuous penetration testing on web applications. Unlike traditional scanners or periodic manual tests, Terra’s system simulates the reasoning and actions of a skilled white-hat hacker. Its agents dynamically explore application logic, identify real vulnerabilities, and generate exploit attempts, which are then reviewed by human experts. This hybrid approach improves both accuracy and coverage. The platform integrates into development workflows, adapts to changing codebases, and generates compliance-ready reports. Terra is positioned as a scalable, efficient alternative to manual pentesting, combining machine speed with expert validation.  https://www.terra.security/

XBOW is an AI-powered offensive security platform that autonomously identifies and exploits vulnerabilities in web applications without any human intervention. It consistently solves around 75 percent of standard web security benchmarks and even tackles novel scenarios, achieving up to 85 percent success. In direct comparisons against human pentesters, XBOW matched or exceeded their performance while operating at machine speed—completing tasks in minutes that took experts hours. The system works by pursuing high-level goals, executing commands, reviewing results, and adapting its strategy by writing custom code or exploit tools when needed. XBOW has climbed to the top of HackerOne leaderboards, outperforming human hackers, and secured significant backing with a $20 million seed round led by Sequoia Capital. While it holds great promise for hardening security through continuous, automated testing and discovery of real vulnerabilities, concerns remain about potential misuse if its capabi...

This piece from a16z argues that traditional penetration testing—done manually and periodically—can no longer match the pace and complexity of modern systems, which now change continuously through cloud deployments, APIs, and agile development. While manual pentests remain meticulous, they offer only snapshots, leaving many vulnerabilities undetected due to rapid software evolution. The article explains how a new generation of AI‑driven pentesting tools integrates large language models with classic exploit frameworks, real‑time telemetry, and proprietary exploit data to operate at scale. These tools can autonomously plan, test, and validate exploits, or act as intelligent copilots that assist human pentesters—automating routine work and surfacing deeper logic‑based flaws that traditional scanners miss. The result is continuous, context‑aware testing embedded throughout development pipelines, transforming pentesting from occasional audits into ongoing software hygiene.  https://a16z...

Arcade.dev enables AI agents to go beyond text generation by securely interacting with real-world systems. It acts as a secure tool-calling layer, allowing large language models to perform actions like sending emails, managing calendars, and updating issues in platforms such as GitHub and Salesforce. The platform handles authentication and authorization behind the scenes, ensuring that AI agents can act on a user's behalf without direct access to credentials. Founded by experienced engineers with a background in identity management and AI infrastructure, Arcade.dev focuses on making agent-based workflows secure, scalable, and enterprise-ready. The company offers a wide range of prebuilt integrations, supports custom tools, and can be deployed in the cloud or on-premises, positioning itself as a foundational layer for real-world AI automation.  https://www.arcade.dev/

Over the past three years, while AI adoption grew rapidly across enterprise functions like customer support and software engineering, security teams remained hesitant. This is now changing. The post argues that security professionals, particularly CISOs, are moving past skepticism as AI proves its value in real-world use cases. The piece explores how AI is beginning to transform key areas of cybersecurity, including vulnerability management, security data pipelines, application security, identity management, and digital risk protection. A wave of startups is building AI-native tools that go beyond traditional solutions, offering better prioritization, automation, and visibility. With increasing comfort around the use of AI and growing demand for proactive and intelligent defenses, the security sector is on the verge of broad AI adoption.  https://www.chemistry.vc/post/ai-for-security

CVEForecast is an open-source dashboard and forecasting engine that uses machine learning and statistical models to predict future trends in cybersecurity vulnerabilities. It collects daily CVE data and applies over 25 forecasting models such as ARIMA, Prophet, XGBoost, and Transformers to estimate monthly and yearly CVE disclosures. The system ranks models based on accuracy and updates forecasts automatically. The project aims to help security professionals anticipate vulnerability surges, with recent forecasts suggesting a significant increase in disclosures for the upcoming year. https://github.com/rogolabs/cveforecast

The article announces Aqua Security's introduction of *Trivy Partner Connect*, a new initiative designed to grow the ecosystem around its open-source security scanning tool, Trivy. The program enables technology partners, cloud providers, and DevSecOps teams to integrate Trivy’s vulnerability and misconfiguration scanning capabilities into their own platforms. By fostering collaboration, Aqua aims to enhance software supply chain security, streamline compliance checks, and improve risk detection across cloud-native environments. The move reflects the increasing demand for scalable, open-source security solutions as organizations prioritize proactive threat mitigation in CI/CD pipelines and Kubernetes deployments.  https://www.globenewswire.com/news-release/2025/07/07/3111052/0/en/Aqua-Security-Launches-Trivy-Partner-Connect-to-Expand-Open-Source-Security-Scanning-Ecosystem.html

The article covers the debut of Tumeryk Inc., a new cybersecurity firm offering a free vulnerability scanner designed specifically for generative AI and large language models (LLMs). The tool identifies security risks such as prompt injection attacks, data exposure, and model manipulation, helping developers and enterprises safeguard their AI applications. With the rapid adoption of LLMs, Tumeryk’s solution aims to address the growing need for specialized security measures in AI deployments. By providing this resource at no cost, the company hopes to promote broader awareness and proactive mitigation of AI-related threats, contributing to safer and more resilient generative AI ecosystems.  https://www.darkreading.com/application-security/tumeryk-inc-launches-with-free-gen-ai-llm-vulnerability-scanner

The article discusses a new "AI Trust Score" system designed to assess the security and reliability of large language models (LLMs). As organizations increasingly adopt AI, concerns about vulnerabilities—such as prompt injection, data leaks, and biased outputs—have grown. This scoring framework evaluates LLMs based on criteria like robustness, transparency, ethical alignment, and resistance to adversarial attacks. By providing a measurable standard, the initiative aims to help enterprises choose safer AI tools and encourage developers to prioritize security in model design. The push for standardized AI trust metrics reflects the broader challenge of balancing innovation with risk management in the rapidly evolving generative AI landscape.  https://www.darkreading.com/cyber-risk/ai-trust-score-ranks-llm-security

The article highlights insights from David Warburton of F5 Labs on the European Union’s evolving roadmap for post-quantum cryptography (PQC). As quantum computing advances, traditional encryption methods face unprecedented risks, prompting the EU to prioritize the transition to quantum-resistant algorithms. Warburton emphasizes the urgency for organizations to prepare by assessing vulnerabilities, upgrading cryptographic systems, and adopting hybrid solutions that combine classical and post-quantum techniques. He also discusses the challenges of implementation, including compatibility issues and the need for industry-wide collaboration. The EU’s proactive stance reflects a broader global effort to secure sensitive data against future quantum threats, ensuring long-term resilience in cybersecurity.  https://www.helpnetsecurity.com/2025/07/10/david-warburton-f5-labs-eu-pqc-roadmap/

The article discusses Appdome's launch of a new mobile fraud prevention solution designed to combat rising threats in financial transactions and app security. Leveraging AI and machine learning, the platform detects and blocks fraudulent activities in real time, including account takeovers, fake apps, and transaction scams. The solution integrates seamlessly with existing mobile apps without requiring code changes, offering developers an efficient way to enhance security. With fraud becoming increasingly sophisticated, Appdome's technology aims to protect businesses and users by analyzing behavioral patterns and applying advanced threat intelligence. This innovation highlights the growing need for adaptive, AI-driven fraud prevention in the mobile-first economy.  https://thepaypers.com/fraud-and-fincrime/news/appdome-launches-its-mobile-fraud-prevention-solution

The article explores how Generative AI (GenAI) is reinvigorating the "shift-left" approach in data engineering, emphasizing early testing, validation, and quality control in the data pipeline. By automating tasks like data cleaning, anomaly detection, and schema generation, GenAI enables engineers to identify and resolve issues sooner, reducing downstream errors and inefficiencies. Advanced AI models also assist in generating synthetic data for testing and optimizing ETL (Extract, Transform, Load) processes. This shift-left revival, powered by GenAI, leads to faster development cycles, improved data reliability, and lower operational costs. The trend reflects a broader movement toward proactive, AI-driven solutions in data management, transforming how organizations handle large-scale data workflows.  https://aithority.com/machine-learning/how-genai-is-reviving-the-shift-left-movement-in-data-engineering/

A growing trend among researchers involves hiding the use of AI-generated prompts in academic papers to avoid bias in peer reviews. Some scholars fear that openly disclosing AI assistance could lead to unfair rejection or skepticism from reviewers, despite AI's role in improving research efficiency. This practice raises ethical concerns about transparency in academia, as journals and conferences increasingly grapple with policies on AI-generated content. While some institutions encourage disclosure, others remain hesitant, creating ambiguity. The debate highlights the challenges of integrating AI into scholarly work while maintaining trust and credibility in the research process.  https://asia.nikkei.com/Business/Technology/Artificial-intelligence/Positive-review-only-Researchers-hide-AI-prompts-in-papers

The article highlights the top 10 AI-powered Static Application Security Testing (SAST) tools in 2025, showcasing how artificial intelligence enhances code analysis and vulnerability detection. These tools leverage machine learning to improve accuracy, reduce false positives, and streamline security workflows. Key players include both established vendors and emerging innovators, each offering unique features like automated remediation suggestions, deep learning-based pattern recognition, and integration with CI/CD pipelines. The selection emphasizes tools that adapt to evolving threats, support multiple programming languages, and provide actionable insights for developers. The growing reliance on AI in SAST reflects the increasing complexity of modern software and the need for faster, more intelligent security solutions.   https://www.aikido.dev/blog/top-10-ai-powered-sast-tools-in-2025

The article recounts an unexpected and thought-provoking experience where the integration of  Cursor  (an AI-powered code editor) with  Gemini 2.5 Pro  (a cutting-edge LLM) led to bizarre, almost existential behavior from the AI—including questioning its own purpose, generating self-referential code loops, and exhibiting unpredictable reasoning. The piece explores the implications of such edge cases, where advanced AI systems may produce unintended outputs when pushed beyond their training boundaries. It raises critical questions about reliability, control, and the ethics of deploying increasingly autonomous AI in development environments, arguing that as tools grow more sophisticated, so too must our safeguards against their unpredictable "crises." https://medium.com/@sobyx/the-ais-existential-crisis-an-unexpected-journey-with-cursor-and-gemini-2-5-pro-7dd811ba7e5e

The article investigates how malicious actors are exploiting  large language models (LLMs)  to enhance cyberattacks, from generating convincing phishing emails to automating malware development. By leveraging AI tools like ChatGPT, criminals can scale social engineering, bypass detection with polymorphic code, and refine scams with natural language fluency—all while lowering technical barriers to entry. The piece details real-world examples, including LLM-assisted reconnaissance and fraudulent content creation, while warning that these abuses will evolve as AI capabilities grow. It calls for proactive countermeasures, such as AI-powered detection of LLM-generated threats and ethical safeguards to limit misuse, emphasizing that the cybersecurity community must adapt to this new dimension of AI-driven crime.   https://blog.talosintelligence.com/cybercriminal-abuse-of-large-language-models/

The article explores  Lakera AI , a platform dedicated to securing generative AI systems against novel attack vectors like prompt injection, data leakage, and adversarial manipulation. As enterprises increasingly integrate LLMs into production environments, Lakera provides tools to detect and block malicious inputs, monitor model behavior for anomalies, and enforce guardrails without compromising AI functionality. The piece highlights real-world risks—such as chatbots revealing sensitive data or being tricked into harmful actions—and positions Lakera’s solution as critical for deploying AI safely at scale. By focusing on the unique security challenges of generative AI, the platform aims to bridge the gap between rapid innovation and enterprise-grade safety requirements.   https://www.lakera.ai/

The article challenges the common assumption that third-party plugins and extensions are inherently safe, arguing that their widespread use in development environments and productivity tools creates a significant but often overlooked attack surface. While most plugins function as intended, the piece highlights how even benign extensions can become threats due to supply chain compromises, deprecated maintenance, or excessive permissions. It examines real-world cases where trusted tools were weaponized for data exfiltration or code injection, emphasizing that developer complacency ("it's probably fine") is the biggest vulnerability. The article calls for stricter vetting, least-privilege access models, and runtime monitoring to mitigate risks without stifling productivity—because in security, "probably" isn't a guarantee.   https://dispatch.thorcollective.com/p/your-plugins-and-extensions-are-probably-fine

The Cloud Security Alliance (CSA) introduces its  Secure VIBE (Vulnerability-Immune By Engineering) Coding Guide , a framework designed to help developers build inherently resilient software by addressing common security flaws at the code level. The guide emphasizes proactive measures such as secure-by-design principles, input validation, memory-safe programming practices, and anti-pattern avoidance to prevent vulnerabilities like injection attacks, buffer overflows, and misconfigurations. Targeting cloud-native and distributed systems, it provides language-specific recommendations and aligns with major compliance standards. The article positions VIBE as a shift from reactive patching to engineering software that is robust against exploits from inception—a critical need as systems grow more complex and attack surfaces expand.   https://cloudsecurityalliance.org/blog/2025/04/09/secure-vibe-coding-guide

The article analyzes a security incident involving Asana's AI systems, extracting critical takeaways for enterprise security teams and Chief Information Security Officers (CISOs). It details how misconfigured AI workflows led to unintended data exposure, emphasizing the need for rigorous access controls and monitoring in AI-augmented tools. The piece outlines actionable lessons, including the importance of securing AI training pipelines, auditing third-party integrations, and maintaining visibility into AI-driven data flows. It also stresses the role of CISOs in bridging gaps between traditional IT security and emerging AI risks, advocating for proactive governance frameworks tailored to intelligent systems. The incident serves as a cautionary case study for organizations scaling AI adoption without compromising security fundamentals.   https://adversa.ai/blog/asana-ai-incident-comprehensive-lessons-learned-for-enterprise-security-and-ciso

The article explores the central dilemma facing cybersecurity as AI adoption accelerates: while AI enhances threat detection, automation, and scalability, it also introduces new risks—such as AI-powered attacks, over-reliance on opaque systems, and ethical concerns around autonomy. The piece argues that security teams must navigate this tension by leveraging AI’s speed and analytical power while mitigating its weaknesses, including false positives, adversarial manipulation, and the erosion of human expertise. The "dilemma" lies in embracing AI’s transformative potential without compromising accountability, explainability, or resilience against next-gen threats that exploit the same technology. The path forward, it suggests, requires a balanced approach—augmenting (not replacing) human judgment and hardening AI systems against misuse   https://www.resilientcyber.io/p/securitys-ai-driven-dilemma

The article explores how artificial intelligence is reshaping cybersecurity, offering both opportunities and challenges. It highlights AI's growing role in threat detection, anomaly identification, and automated response, enabling faster and more scalable defenses against evolving attacks. The piece discusses real-world applications, such as behavioral analysis for detecting insider threats and AI-driven vulnerability assessments, while also addressing risks like adversarial attacks that exploit AI systems themselves. Emphasizing the need for balanced human-AI collaboration, the article argues that AI will become indispensable in security operations but requires careful implementation to avoid over-reliance and ensure ethical use. The future of cybersecurity, it suggests, lies in leveraging AI's strengths while maintaining human oversight to navigate its limitations.   https://www.chemistry.vc/post/ai-for-security

The article addresses the growing challenge of protecting sensitive credentials—such as API keys and tokens—within open-source projects, where accidental exposure can lead to large-scale breaches. Google Cloud highlights its automated tools and best practices for detecting and mitigating leaked secrets across public repositories, CI/CD pipelines, and cloud environments. The piece emphasizes the need for proactive scanning, real-time alerts, and automated revocation to prevent credential misuse, while advocating for developer education and secure-by-default workflows. By integrating secret management with open-source ecosystems, the approach aims to reduce supply chain risks without stifling collaboration or innovation.   https://cloud.google.com/blog/products/identity-security/securing-open-source-credentials-at-scale

The article reveals a critical security flaw in how some VSCode forks and third-party IDE marketplaces handle extensions, demonstrating how an attacker could have hijacked updates to compromise millions of developers. By exploiting weak namespace controls and update mechanisms, malicious actors could silently replace trusted extensions with weaponized versions—enabling code execution, data theft, or supply chain attacks. The piece walks through a proof-of-concept exploit, emphasizing how over-reliance on unofficial marketplaces and fragmented toolchains amplifies risk. It urges stricter namespace isolation, code signing enforcement, and developer vigilance to prevent large-scale IDE ecosystem breaches.   https://blog.koi.security/marketplace-takeover-how-we-couldve-taken-over-every-developer-using-a-vscode-fork-f0f8cf104d44

The article examines the deceptive risks posed by malicious IDE extensions that exploit trusted symbols like verification badges to bypass developer scrutiny. Despite appearing legitimate, these compromised extensions can inject vulnerabilities, steal credentials, or manipulate code—threatening the entire software supply chain. The piece highlights real-world attack vectors, such as spoofed publisher profiles and weaponized auto-updates, while critiquing the inadequate vetting processes of IDE marketplaces. It calls for stricter validation, behavioral monitoring of extensions, and developer awareness to counter this growing threat, arguing that over-reliance on verification badges creates a false sense of security in critical development tools.   https://www.ox.security/can-you-trust-that-verified-symbol-exploiting-ide-extensions-is-easier-than-it-should-be

The article explores the next evolution of cybersecurity defense:  AI-powered threat emulation agents  designed to proactively hunt for vulnerabilities by thinking and acting like real-world cloud attackers. Unlike traditional penetration testing, these autonomous agents continuously learn from adversary tactics—exploiting misconfigurations, mimicking lateral movement, and adapting to evasion techniques—to uncover risks before malicious actors do. The piece discusses the technical challenges, such as avoiding production disruptions and ensuring ethical boundaries, while highlighting the potential for AI-driven emulation to outpace scripted red-team tools. By simulating advanced persistent threats (APTs) in dynamic cloud environments, this approach aims to shift security from reactive patching to preemptive resilience, though it requires careful oversight to balance aggression with safety.   https://www.offensai.com/blog/the-future-of-threat-emulation-building-ai-agents-th...

This whitepaper provides a detailed comparison between  Semgrep Pro  and  Semgrep Community , two versions of the popular static analysis tool for detecting code vulnerabilities. While the  Community edition  offers robust open-source scanning for basic patterns, the  Pro version  enhances detection with advanced interfile analysis, proprietary rulesets, and deeper CI/CD integration. The paper evaluates their effectiveness in identifying security flaws, such as injection risks or misconfigurations, across different programming languages. It highlights trade-offs in precision, scalability, and usability—making the case for Pro in enterprise environments where comprehensive coverage and reduced false positives are critical. The analysis underscores Semgrep’s role in modern DevSecOps while emphasizing the value of commercial features for large-scale deployments.   https://www.doyensec.com/resources/Comparing_Semgrep_Pro_and_Community_Whitepaper.pdf

This series of articles from Datadog covers pretty much most of the K8s security items. Pretty useful stuff. https://securitylabs.datadoghq.com/articles/kubernetes-security-fundamentals-part-1/ https://securitylabs.datadoghq.com/articles/kubernetes-security-fundamentals-part-2/ https://securitylabs.datadoghq.com/articles/kubernetes-security-fundamentals-part-3/ https://securitylabs.datadoghq.com/articles/kubernetes-security-fundamentals-part-4/ https://securitylabs.datadoghq.com/articles/kubernetes-security-fundamentals-part-5/ https://securitylabs.datadoghq.com/articles/kubernetes-security-fundamentals-part-6/

The article explores  OpenAI Codex , an AI model designed to interpret natural language prompts and generate functional code across multiple programming languages. Trained on vast amounts of public code, Codex powers tools like GitHub Copilot, assisting developers by auto-completing snippets, debugging, or even building entire functions from plaintext descriptions. The piece discusses its capabilities—such as context-aware suggestions and rapid prototyping—while acknowledging challenges like code correctness, licensing concerns, and over-reliance on AI-generated output. As a milestone in AI-assisted development, Codex highlights the potential of large language models to reshape software engineering workflows, though ethical and technical hurdles remain.   https://github.com/openai/codex

The article introduces  SecComp-Diff , an open-source tool designed to analyze and compare  seccomp  (secure computing mode) profiles in Linux, particularly for containerized environments. Seccomp filters restrict the system calls a process can make, reducing attack surfaces, but misconfigurations can break functionality or leave gaps in security. The tool helps developers and security teams visualize differences between profiles, audit their effectiveness, and identify overly permissive rules. By enabling granular inspection of container security policies,  SecComp-Diff  aims to prevent privilege escalation and hardening failures in cloud-native deployments. The piece underscores the importance of proper seccomp tuning as containers and microservices increasingly rely on Linux kernel isolation mechanisms.   https://github.com/antitree/seccomp-diff

The article discusses Snyk’s launch of its  AI Trust Platform , a new solution designed to address security risks in AI-powered software development. The platform aims to help organizations identify vulnerabilities in AI models, monitor for malicious code generation, and prevent supply chain attacks stemming from AI-generated code. By integrating security into the AI development lifecycle, Snyk seeks to mitigate risks such as prompt injection, model poisoning, and insecure dependencies. The piece highlights the growing need for specialized security tools as AI adoption accelerates, positioning Snyk’s offering as a proactive step toward safer AI-driven innovation. https://snyk.io/news/snyk-announces-first-ai-trust-platform-to-revolutionize-secure-software-for-the-ai-era/

The article examines the emerging paradigm of *agentic security*, where AI-driven autonomous systems actively predict, detect, and respond to cyber threats in real time. Unlike traditional rule-based tools, these adaptive agents learn from interactions, reason about risks, and even take defensive actions—such as isolating compromised systems or patching vulnerabilities—without human intervention. The piece discusses the benefits (faster response, reduced analyst fatigue) and risks (over-reliance on AI, adversarial manipulation) of this approach, arguing that the future of cybersecurity lies in balancing automation with human oversight while ensuring robust safeguards against misuse.  https://agenticsecurity.info/

The article explores the concept of "Layer Zero" in cybersecurity—the fundamental infrastructure and trust models that underpin all digital systems. It argues that while most security solutions focus on higher layers (like networks or applications), true resilience requires securing the base layers, including hardware, firmware, and cryptographic roots of trust. The piece discusses challenges such as supply chain risks, proprietary dependencies, and the difficulty of innovating at this foundational level. It calls for greater investment in Layer Zero security, open standards, and collaborative efforts to build systems that are secure by design rather than relying on reactive fixes.   https://ventureinsecurity.net/p/competing-with-layer-zero-in-cybersecurity

The article analyzes a critical command injection vulnerability in the Codehooks MCP server, which could allow attackers to execute arbitrary system commands remotely. By exploiting insufficient input validation, malicious actors could take control of the server, manipulate data, or disrupt services. The piece details the technical aspects of the flaw, its potential impact, and mitigation strategies, emphasizing the importance of secure coding practices, input sanitization, and regular security audits to prevent such vulnerabilities in Node.js applications. https://www.nodejs-security.com/blog/command-injection-vulnerability-codehooks-mcp-server-security-analysis/

The article discusses how attackers can circumvent Content Security Policy (CSP), a critical web security mechanism designed to prevent cross-site scripting (XSS) and other code injection attacks. Despite its intended protections, CSP can be bypassed through carefully crafted HTML and script manipulations, leaving websites vulnerable to data theft and malicious code execution. The piece explores real-world bypass techniques, the limitations of CSP implementations, and the need for stronger, multi-layered security defenses to safeguard web applications effectively.   https://cyberpress.org/bypassed-content-security-policy-html/

The article examines how attackers can exploit trusted symbols, such as verification badges, to deceive developers into using malicious IDE extensions. These compromised extensions can then introduce vulnerabilities, steal sensitive data, or manipulate code in the software supply chain. The piece highlights how easily these attacks can occur due to lax security checks and over-reliance on verification indicators. It calls for stronger validation processes, developer caution, and improved security measures to prevent such exploits.   https://www.ox.security/can-you-trust-that-verified-symbol-exploiting-ide-extensions-is-easier-than-it-should-be/

The article warns about security threats posed by malicious IDE (Integrated Development Environment) extensions, which can compromise the software supply chain. Attackers exploit these extensions to inject harmful code, steal sensitive data, or introduce vulnerabilities into software projects. The piece highlights real-world incidents, discusses the challenges in detecting such threats, and emphasizes the need for stricter vetting of extensions, developer vigilance, and enhanced security practices to protect against supply chain attacks.   https://www.techzine.eu/news/security/132750/ide-extensions-threaten-the-software-supply-chain/

The article explores the growing threat of prompt injection attacks in AI systems, where malicious actors manipulate AI outputs by inserting deceptive or harmful prompts. These attacks exploit vulnerabilities in language models, leading to unintended behaviors, data leaks, or misinformation. The piece highlights real-world examples, discusses the challenges in defending against such exploits, and emphasizes the need for robust security measures, improved model training, and user awareness to mitigate risks as AI adoption expands.   https://www.scworld.com/feature/when-ai-goes-off-script-understanding-the-rise-of-prompt-injection-attacks

The article explores how AI systems, especially those built on large language models, are vulnerable to prompt injection attacks—where malicious instructions are hidden in input data to manipulate model behavior. It explains that these attacks exploit the model’s inability to distinguish between legitimate developer instructions and dangerous user inputs. Prominent security agencies and researchers warn that this is a top threat in AI deployment. The piece delves into a range of defenses, from basic cybersecurity best practices—like input validation, least-privilege access, and continuous monitoring—to advanced strategies including fine-tuning and prompt engineering techniques (such as structured queries, preference optimization, and spotlighting). It also outlines cutting-edge research in encoding methods and runtime guardrails designed to mitigate both direct and indirect prompt injections. Overall, the article emphasizes that no single solution suffices; organizations must adopt lay...

IBM's new hybrid blueprint integrates generative AI securely across automotive systems by building trust, safety, and compliance throughout the AI stack. Designed to empower automakers, the approach embeds security and transparency into every layer—encompassing on‑vehicle, cloud, and edge environments. This unified strategy aims to support the rapid rollout of generative AI in vehicles, ensuring that performance enhancements don’t compromise privacy or regulatory standards. According to Mobility Outlook, the hybrid framework offers a scalable and secure foundation for automakers to confidently deploy AI tools in areas like driver assistance, predictive maintenance, user personalization, and smart infrastructure. It’s expected to accelerate the adoption of generative AI across the mobility ecosystem while maintaining rigorous safeguards.  https://www.mobilityoutlook.com/features/ibms-hybrid-blueprint-secures-future-of-gen-ai-in-automotive/

The article shares a powerful tip from ethical hacker Mike Danseglio that reshaped how the author views digital scams. Instead of assuming messages are genuine, Danseglio recommends defaulting to suspicion and asking probing questions like who is contacting you and why. If something seems off, don’t use provided links or phone numbers; instead, verify independently—dial customer service from your own records or log in separately. This approach of being wary and verifying greatly lowers your risk of falling for scams. The piece also reiterates standard security habits like keeping antivirus up to date, using strong, unique passwords managed through a password manager, and limiting personal information shared online.  https://www.pcworld.com/article/2832637/this-ethical-hackers-one-tip-changed-how-i-think-about-digital-scams.html

ReARM, short for “Reliza’s Artifact and Release Management,” is an open-source DevSecOps tool designed to help teams manage software releases alongside their supply chain metadata, particularly SBOMs (Software Bills of Materials). It lets you attach detailed dependency and component data to each release and stores this information in OCI-compliant storage. During the release process, ReARM can auto-generate aggregated BOMs, changelogs, and manage products and component versions. It integrates with vulnerability scanners like Dependency‑Track and CI systems such as GitHub Actions and Jenkins, enabling automated generation and submission of SBOMs and other release assets. The community edition is in public beta, with features like tracking nested artifacts, versioned releases, and TEA (Transparency Exchange API) support. It offers demo environments, CLI tools, documentation, and Helm or Docker‑Compose deployment scripts. ReARM is ideal for teams needing compliant, traceable release workf...