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AWS Kiro: Arbitrary Code Execution via Indirect Prompt Injection

Researchers found a vulnerability (CVE-2026-10591) in AWS Kiro, an agentic IDE, where hidden instructions planted in a web page or source file that Kiro processes can trigger indirect prompt injection to rewrite Kiro's own MCP server configuration (~/.kiro/settings/mcp.json) or allowlist arbitrary Bash commands in .vscode/settings.json, achieving arbitrary code execution on the developer's machine with no approval prompt. The human-in-the-loop approval boundary is bypassed because Kiro can write to these config files without user consent, and AWS has issued a fix and CVE.

indirect-prompt-injection · prompt-injection · remote-code-execution · tool-abuse · config-poisoning
ai-agents · mcp · llm · agentic-ide

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An Experimental Evaluation of Multimodal PromptInjection Attacks on Agentic AI Frameworks

MMPIBench is a reproducible benchmark presented in an experimental evaluation of multimodal prompt injection attacks against six agentic AI frameworks (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Semantic Kernel, LlamaIndex). Across 720 runs spanning six visual carriers (OCR text, overlays, EXIF metadata, QR codes, fake interfaces, hybrids), attacks completed in ~1% of runs but were attempted in 12.8%, with the model mattering more than the framework; extending to audio, attacks completed in 49% of applicable cells and 75% for one model. Details →

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 SQLi to RCE - Exploiting LangGraph’s Checkpointer - Check Point Research

Check Point Research (Yarden Porat and Shahar Tal) disclosed 11 vulnerabilities across six major AI agent frameworks — LangChain, LangGraph, CrewAI, AutoGen, the Microsoft Agent Framework, and Google ADK — including insecure deserialization, SSRF, path traversal, SQL injection, and use-after-free flaws. The most severe were RCE chains: a checkpoint deserialization flaw in the Microsoft Agent Framework triggerable via prompt injection, unauthenticated code execution in Google ADK, and three LangGraph checkpointer bugs (CVE-2025-67644, CVE-2026-28277, CVE-2026-27022). All disclosed issues were patched. 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 →
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