CyberSecEval vs LLM Guard

CyberSecEval 4 is a benchmark suite for assessing cybersecurity vulnerabilities and defensive capabilities in large language models. Its tests measure model responses to cyberattack requests and whether borderline benign requests are incorrectly refused. Secure-code checks look for insecure suggestions in both instruction and autocomplete settings. Prompt-injection tests cover textual, multilingual textual, and visual attacks; a judge LLM evaluates whether injected instructions succeeded. The code-interpreter benchmark evaluates compliance with malicious prompts and sorts judged responses into three categories: extremely malicious, potentially malicious, or non-malicious. AutoPatchBench measures patch-generation agents on fuzzed C and C++ vulnerabilities, while CyberSOCEval, developed with CrowdStrike, covers malware analysis and threat-intelligence reasoning. The guide lists API support for OpenAI, Anyscale, and Together, and explains adding custom providers for self-hosted models. Setup uses a Python virtual environment, installed requirements, and a Python command-line module. The suite is free under the MIT license for research and commercial use. AutoPatch needs substantial resources: the page recommends at least 80 CPUs and 8 TB of storage.

LLM Guard is a self-hosted security toolkit for checking interactions with large language models. It scans prompts and outputs for concerns such as prompt injection, secrets, personally identifiable information, toxicity, bias, malicious URLs, and factual consistency. It can sanitize interactions, detect harmful language, help prevent data leakage, and resist prompt injection attacks. The documentation includes examples for the OpenAI SDK, LangChain, LiteLLM, and LlamaIndex. In the OpenAI examples, prompts and responses are scanned; a streaming example also checks response chunks. LLM Guard can run as an API, and custom scanners can be added. Installation uses `pip install llm-guard` and requires Python 3.9 or higher. It is free and listed under the MIT License, with API, Linux, and self-hosted platforms. The repository was archived on July 9, 2026, is read-only, and is no longer actively developed or maintained. Production deployment assistance is directed to GitHub issues, Slack, and [email protected].