Check Point AI Guardrails vs CyberSecEval

Check Point AI Guardrails analyzes large-language-model inputs and outputs for prompt injection, data leakage, policy bypass attempts, unsafe content, and related AI threats. It provides threat intelligence, analytics, and developer tooling for securing AI applications. Deployment can be on premises or in a private cloud, with processed personal data kept in the customer’s controlled environment except for agreed support or maintenance. Check Point Firewalls can apply the service to generative and agentic AI traffic, including content moderation and MCP tool responses. Listed supported services include OpenAI, Claude, Gemini, and Mistral AI through developer APIs. A Copilot Studio integration combines runtime guardrails with data-loss prevention and threat prevention. The cloud service may process prompts, messages, tool information, enabled uploads, model outputs, and security analytics. Pricing is on request; retention lasts through the subscription and three months afterward.

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.