LangChain has incomplete f-string validation in prompt templates
LangChain Text Splitters: HTMLHeaderTextSplitter.split_text_from_url SSRF Redirect Bypass
LangChain is a framework for building agents and LLM-powered applications. Prior to 1.3.9, several LangChain components that resolve filesystem paths or expand search patterns do not consistently confine
LangChain is a framework for building agents and LLM-powered applications. Prior to langchain-text-splitters 1.1.2, HTMLHeaderTextSplitter.split_text_from_url() validated the initial URL using validate_safe
LangChain is a framework for building agents and LLM-powered applications. Prior to 0.3.84 and 1.2.28, LangChain's f-string prompt-template validation was incomplete in two respects. First, some
LangChain: Path traversal and sandbox escape in LangChain file-search middleware and loaders
LangChain is a framework for building LLM-powered applications. Prior to 1.3.1, MongoDBChatMessageHistory does not enforce the documented string type for an untrusted structured session identifier at runtime, allowing
options lists. The eval() sink is triggered when a component is converted into a LangChain tool via ComponentToolkit.get_tools(), including during custom component saves through the API, by interpolating options
The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirements
principles derived from a compositional model of agentic architectures, we audit three dominant frameworks (LangChain, AutoGPT, and OpenAI Agents SDK) and find no native compliance in any of them. Memory
vulnerability was detected in chatchat-space Langchain-Chatchat up to 0.3.1.3. This vulnerability affects the function files/list_files/retrieve_file/retrieve_file_content/delete_file of the file libs/chatchat-server/chatchat/server/api_server/openai_routes.py of the component Compatible File Service. The manipulation results
LangChain is a framework for building LLM-powered applications. Prior to 1.1.14, the RecursiveUrlLoader class in @langchain/community is a web crawler that recursively follows links from a starting
Toward Trustworthy Agentic AI: A Multimodal Framework for Preventing Prompt Injection Attacks
Large Language Models (LLMs), Vision-Language Models (VLMs), and new agentic AI systems, like LangChain and GraphChain. Nevertheless, this agentic environment increases the probability of the occurrence of multimodal prompt
stored cross-site scripting (XSS) vulnerability in @n8n/n8n-nodes-langchain.chatTrigger. An authorized user can configure the LangChain Chat Trigger node with malicious JavaScript in the initialMessages field and enable public access
vulnerability classified as problematic was found in chatchat-space Langchain-Chatchat up to 0.3.1. This vulnerability affects unknown code of the file /v1/files?purpose=assistants. The manipulation leads to path
vulnerability in langchain-core versions >=0.1.17,<0.1.53, >=0.2.0,<0.2.43, and >=0.3.0,<0.3.15 allows unauthorized users to read arbitrary files from the host file system. The issue arises from the ability
vulnerability in the langchain-ai/langchain repository allows for a Billion Laughs Attack, a type of XML External Entity (XXE) exploitation. By nesting multiple layers of entities within
Proof-of-Execution Memory: Defending LLM Agents Against Forged-Reasoning Attacks by Verifying What Actually Happened
attacks aimed at itself, adds microseconds of overhead, and works unchanged in a real LangChain agent. PoEM protects exactly the decisions it gates
Robust Agent Compensation (RAC): Teaching AI Agents to Compensate
existing agent frameworks via their existing extension points. We present an implementation based on LangChain, demonstrate its viability through the $τ$-bench and REALM-Bench, and show that when solving
LangSmith Client SDKs has Prototype Pollution in langsmith-sdk via