A Dev.to report using the open-source AgentGuard security scanner evaluates three widely used AI agent frameworks—LlamaIndex, Microsoft AutoGen, and CrewAI—and finds large numbers of issues across their codebases. In the scan, LlamaIndex is associated with the highest level of findings, including 252 critical issues out of 1,003 high and medium results, across 2,951 files. The report describes common problem categories such as unbounded recursive agent execution, data exfiltration paths, and trust boundary violations. For Microsoft AutoGen, the scan identifies 80 critical issues, with highlighted concerns including Docker sandbox escaping risk due to host filesystem mounts, credential exposure in replay logs, and unsafe prompt trust patterns between an MCP host and server prompts. A second Dev.to post also states that the author opened three GitHub issues in the AutoGen and LlamaIndex repositories corresponding to container escape, self-modification behavior, and recursive execution concerns. CrewAI is reported to have fewer critical findings, with 391 medium findings and no critical or high issues reported. Across the articles, the author attributes the issues to recurring architectural gaps in trust boundaries, recursion/loop controls, and agent self-modification constraints.
Security scan finds many vulnerabilities in LlamaIndex and AutoGen agent frameworks
A Dev.to report using the open-source AgentGuard security scanner evaluates three widely used AI agent frameworks—LlamaIndex, Microsoft AutoGen, and CrewAI—and finds large numbers of issues across the...
- A Dev.to author scans LlamaIndex, Microsoft AutoGen, and CrewAI using AgentGuard and reports hundreds of security findings, including critical issues.
- For LlamaIndex, the scan reports 252 critical findings, including allegations of unbounded recursive execution and potential data exfiltration and trust boundary violations.
- For Microsoft AutoGen, the scan reports 80 critical findings; highlighted examples include unsafe Docker sandboxing (host filesystem mounts), agent self-modification patterns, and credential exposure in replay logs.
- The author also reports opening three GitHub issues: two in microsoft/autogen and one in run-llama/llama_index, corresponding to container escape, self-modification, and recursive execution concerns.
- CrewAI is reported to have 0 critical and 0 high findings in the scan, with 391 medium findings related to data exfiltration patterns.
I just opened 3 security issues on two of the most popular AI agent frameworks on GitHub (combined 110K+ stars). The Issues microsoft/autogen#7917: Docker code executor mounts host filesystem into sandboxed containers without trust boundary validation — container escape vector. microsoft/autogen#7918: Agent self-modification patterns in Canvas memory module — agents can alter their own operating constraints during execution. run-llama/llama_index#22245: 441 instances of unbounded recursive agent execution across 2,951 files — systemic resource exhaustion risk. All found with AgentGuard v0.6.2 (pip install dfx-agentguard), an open-source AI agent security scanner. Why Issues, Not Articles I have published 12 articles on Dev.to. Average views: 11. GitHub Issues on 50K+ star repos are read by thousands of developers and stay visible for years. This is the correct distribution channel for security findings — direct, unfiltered, and actionable. The Pattern The same vulnerability classes appear across all frameworks: Trust boundary violations (ASI10): agents crossing filesystem and network boundaries Agent recursion (ASI09): unbounded loops without circuit breakers Self-modification (ASI10): agents modifying their own state during execution These are not framework-specific bugs. They are systemic architectural gaps in how we build autonomous agents. Every framework needs guardrails for resource limits, trust boundaries, and behavioral constraints. AgentGuard detects all of them. 16 rules, 83 tests, 36 benchmark samples, 100 percent detection rate. pip install dfx-agentguard
3 hours agoI scanned three of the most popular AI agent frameworks with AgentGuard v0.6.1. The results were worse than I expected. The Scan Framework Files Findings CRITICAL HIGH MEDIUM LlamaIndex 2,951 1,003 252 558 193 AutoGen 549 229 80 113 36 CrewAI 84 391 0 0 391 LlamaIndex (252 CRITICAL) The most popular RAG framework: 252 critical findings. 441 agent loop patterns, 178 data exfiltration paths, 141 trust boundary violations. AutoGen (80 CRITICAL) -- Microsoft Self-modification vectors. Credential exposure in replay logs. MCP host trusts server prompts unsafely. Docker executor mounts host filesystem into sandbox. CrewAI (391 MEDIUM) Data exfiltration patterns across 391 locations -- agent data flowing to external endpoints without constraints. What This Means Frameworks with 30K+ stars, Fortune 500 production deployments. Findings in the code that ships today. Every finding has a clear fix -- input validation, Pydantic models, sandbox enforcement, log scrubbing. Solved application security problems not yet applied to AI agent code. pip install dfx-agentguard GitHub: https://github.com/dockfixlabs/agentguard Benchmark: 36 samples, 100 percent detection, 0 FP
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