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Black Hat 2026: AI Agent Framework Flaws Expose Secrets

Check Point researchers Shahar Tal and Yarden Porat presented at Black Hat 2026 an audit of major AI agent frameworks — LangChain, CrewAI, Microsoft Agent Framework and Google's ADK — uncovering 21 findings across eight codebases including 12 CVEs. The flaws are classic vulnerability classes (unsafe deserialization, SSRF, SQL injection, sandbox escape, arbitrary file read, memory corruption, PDF-parser RCE) reachable via post-injection exploitation, where attacker-controlled content poisons an agent's memory and triggers the framework's own internal plumbing to steal credentials and data without calling dangerous functions directly. Details →

Prompt injection isn't the bug, AI agent frameworks are

Check Point researchers Yarden Porat and Shahar Tal disclosed 11 vulnerabilities across major AI agent frameworks (LangChain, LangGraph, CrewAI, AutoGen, Microsoft Agent Framework, Google ADK), arguing that the real risk is how frameworks handle prompt injection rather than injection itself. Their findings include classic flaw classes—insecure deserialization, SSRF, path traversal, use-after-free—such as a critical checkpoint deserialization bug in Microsoft Agent Framework that allowed remote code execution via poisoned agent state; Microsoft paid a $10,000 bounty and fixed it. Details →

Demystifying RCE Vulnerabilities in LLM-Integrated Apps | Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security

Academic paper "Demystifying RCE Vulnerabilities in LLM-Integrated Apps" (ACM CCS 2024) introduces LLMSmith, a system combining lightweight static analysis and prompt-based exploitation to find and exploit remote code execution flaws triggered via prompt injection in LLM app frameworks like LangChain. The authors uncovered 20 vulnerabilities across 11 frameworks (19 RCE, 1 file read/write), with 13 assigned CVEs (6 at CVSS 9.8), and successfully attacked 17 of 51 affected apps demonstrating data leakage and full system takeover. Details →

Capability Gates Are Not Authorization: Confused-Deputy Failures in LLM Agent Frameworks

A security research paper, 'Capability Gates Are Not Authorization,' audits LangChain/LangGraph, LlamaIndex, and the Stripe Agent Toolkit and finds that all three provide capability gating by default but none enforce a deterministic fail-closed per-call value authorization gate, enabling classic confused-deputy abuse where an attacker-influenced model emits an unauthorized side-effecting call (e.g., a payout). The authors introduce SCOPEGATE, a five-stage PDP/PEP control (scope, authorization, money ceiling, idempotency, default deny), and report an identical unauthorized payout executing under LangChain's default dispatch but denied by SCOPEGATE, with an available artifact (github.com/raceksd-source/scopegate-runtime). Details →
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