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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 →

From Prompt Injection to Web Exploitation: Revisiting Classic Vulnerabilities in LLM-Integrated Applications

A research paper by Spiros Tsigkopoulos and Christoforos Ntantogian introduces "LLM-mediated web attacks," a class where attacker-controlled input is transformed by an LLM-integrated application and then reaches traditional web-app sinks, systematized as LLM2X variants (LLM2SQLi, LLM2XSS, LLM2SSTI, LLM2CommandInjection, LLM2IDOR, LLM2CSRF, LLM2XXE, LLM2SSRF). The authors implement TicketOracle, a Flask-based LLM-integrated app, to evaluate LLM2SSRF across five scenarios and seven LLMs, finding substantial model-specific variation in susceptibility. Details →
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