Appsec adventures

This blog post challenges the narrative that AI is making vulnerability discovery a cheap commodity, arguing that while AI has automated shallow discovery and exploit reproduction, the most valuable vulnerabilities remain scarce and increasingly expensive. The author points to market evidence—broker prices for top exploits have risen from tens of thousands a decade ago to $7–$9 million today—demonstrating that discovery is not commoditizing, because valuable vulnerabilities follow a power-law distribution where the critical, exclusive findings that matter are outside the reach of generic, shared AI tools. The exclusivity window is the entire source of a vulnerability's value, and when everyone queries the same public models, discovery becomes correlated and worthless for high-stakes defense. The post further explains that the "tail" of difficult-to-find vulnerabilities moves continuously, as each new capability raises the floor but exposes a new ceiling, and that the apex...

This article argues that the traditional, gate-based software security model is being structurally dismantled by the rise of AI coding agents that generate, review, and deploy code at machine speed, with a documented 37.6% increase in critical vulnerabilities after five agent iterations without embedded security checks. The author contends that while SAST remains necessary, it is insufficient, and the real crisis is governance failure—as demonstrated by 100% of surveyed companies having AI-generated code in production while 81% of security teams lack visibility into it. The solution lies in a "shift-everywhere" architecture with three layers: embedded agent-native security (using MCP servers and policy files as critical infrastructure), gate-based controls for audit and AI-assisted remediation, and runtime behavioral monitoring with agent identity and least-privilege principles. The paper calls for concrete changes: updating SSDLC policies with an "agentic annex" co...

This blog post details how Figma has integrated AI agents across three stages of its software development lifecycle—code generation, pull request (PR) review, and historical code auditing—to proactively find and fix vulnerabilities. The authors emphasize that while pointing agents at code is simple, achieving the required precision (low false positives) and recall (catching real bugs) is a hard engineering challenge. Their approach centers on building a comprehensive, 99-line security policy containing 68 precedents, which acts as the shared threat model for all agents and was iteratively refined by hand-labeling false positives until precision exceeded a 70% threshold. In production, the reviewer runs on every PR using multiple models, costs about $0.50 median per review, and uses an adjudicator pass to improve recall. The system includes robust metrics: recall is measured against a corpus of 66 known past vulnerabilities (with a current union catch rate of 75.8%), precision is monito...

This Microsoft Security blog post addresses the identity and authorization challenges posed by AI agents that plan, chain actions across systems, and invoke tools without explicit human approval for each step. It argues that organizations must treat every agent as a first-class principal with a lifecycle-managed identity, moving beyond shared secrets or broad roles that lead to "scope creep" and ambiguous accountability. The article outlines a set of best practices: assign a dedicated agent identity with clear ownership and purpose; implement least-privilege, task-based RBAC scoped to specific resources and operations; use safe tool binding with explicit allowlists for approved actions; and ensure end-to-end auditability with logs capturing agent identity, role, scope, resource, and action. It recommends just-in-time (JIT) elevation for temporary higher privileges, maintaining a minimal baseline role, and applying hard authorization boundaries instead of relying on prompts. T...

Suzaku is an open-source tool, written in memory-safe Rust, designed for threat hunting and generating forensic timelines from cloud logs, with a primary focus on AWS CloudTrail (with Azure and GCP planned). It uses native Sigma detection rules to identify malicious activity among thousands of cloud API calls, filtering out noise to produce a concise, attack-relevant timeline. The output includes attacker activity summaries with key details like source IPs, geolocation, regions, and user agents to aid investigation and pivoting. Created by Yamato Security, Suzaku is positioned as a cloud-centric counterpart to Hayabusa (a Windows event log analyzer) and is available under the GNU AGPLv3 license, with comprehensive multi-language documentation and binaries available on its releases page.  https://github.com/Yamato-Security/suzaku

nono is a tool that creates instant, zero-latency sandboxes for AI agents like Claude Code, Codex, and Copilot on macOS, Linux, and Windows (WSL2) without requiring daemons, containers, VMs, or disk space. It enforces a least-privilege policy by default, granting agents read/write access only to the current directory and isolating them from SSH keys, cloud credentials, and the rest of the system. Its advanced feature allows granular control over tools called by agents (e.g., `git`, `gh`, `curl`), where each tool can run in its own isolated child sandbox with separate filesystem grants, network rules, and credentials, preventing an agent from widening its own privileges. Profiles for popular agents are available through a registry and can be customized using a composable JSON policy system. The project, built by the team behind Sigstore, provides FFI bindings for Rust, Python, TypeScript, and Go, and is used by large technology companies to operationalize agent security in production wo...