langchain
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
60
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Critical
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tessl review fix ./14-agents/langchain/SKILL.mdSecurity
1 critical severity finding. Installing this skill is not recommended: please review these findings carefully if you do intend to do so.
E006: Malicious code pattern detected in skill scripts.
What this means
Detected high-risk code patterns in the skill content — including its prompts, tool definitions, and resources — such as data exfiltration, backdoors, remote code execution, credential theft, system compromise, supply chain attacks, and obfuscation techniques.
Why it was flagged
The documentation contains multiple high-risk patterns that enable remote code execution and potential data exfiltration (eval on user input, shell/Python REPL tools, dangerous deserialization flags, and automatic tracing to an external observability service), which could be abused as backdoors or for credential/data leakage.
Low
Low-risk findings.
2 low severity findings. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
RAG flows in SKILL.md include runtime web document loading via `WebBaseLoader` (e.g., `loader = WebBaseLoader("https://docs.python.org/...")` then `docs = loader.load()`), which fetches public web pages whose extracted text is then passed into the LLM context through the RetrievalQA/ConversationalRetrievalChain “stuff”/retrieval context.
W012: Unverifiable external dependency detected (runtime URL that controls agent).
What this means
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
Why it was flagged
The examples call WebBaseLoader(...).load() on external pages (e.g., "https://docs.python.org/3/tutorial/", "https://docs.python.org", "https://docs.numpy.org", "https://example.com"), which are fetched at runtime and injected into the RAG/agent context used to prompt the LLM.
- Repository
- Orchestra-Research/AI-Research-SKILLs
- Commit
773a529
- Audited
- Security analysis
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