First reported · updated · 2 reports checkpoint.com
Lead dispatch
First reported · updated · 3 reports embracethered.com
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
Severity
0.85
The wire · latest
First reported howtoharden.com
LangChain Hardening Guide
The LangChain Hardening Guide on howtoharden.com is a defensive reference covering security hardening for LangChain, LangSmith, and LangGraph, including SSO/RBAC, SDK CVE patching (CVE-2026-25528 SSRF, CVE-2026-25750 token leak), prompt-injection defense mapped to OWASP LLM Top 10, tracing redaction, sandboxing untrusted code, and agent tool least-privilege. The guide is tiered (L1-L3) and links companion code packs of hardening scripts on GitHub. Details →First reported theregister.com
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 →First reported auth0.com
Agentic Loops and Multi-Agent Graphs Expand AI Prompt Injection Risk
Auth0 published an analysis arguing that the most serious AI agent security risks stem from architectural choices—specifically agentic loops and multi-agent graphs—rather than the model alone. In loop-based systems attacker-controlled external content can be fed back into an agent's reasoning to persist and compound malicious instructions, while multi-agent graphs create trust-boundary failures where a compromised agent passes tainted instructions downstream; the report cites 2024 'Prompt Infection' research showing prompt injection can self-replicate across connected agents and recommends controls like step/time budgets, approval gates, scoped permissions, and treating tool output as untrusted. Details →First reported · updated · 2 reports arxiv.org
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 →How the wire is made
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