CyberSecEval vs GLACIS
CyberSecEval
meta-llama.github.ioGLACIS
glacis.ioSide by side
| Figure | CyberSecEval | GLACIS |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | Yes |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · LLM Security Tools | 8.9 (#1) | 7.8 (#9) |
| What it does | CyberSecEval is Meta’s benchmark suite for measuring cybersecurity risks and defensive capabilities of large language models. | Glacis places runtime controls in the path of AI actions and creates signed records of covered decisions for external verification. |
Plans
In their own words
CyberSecEval
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.
GLACIS
GLACIS puts controls in the path of AI actions and creates signed records of covered decisions for external verification. When an action crosses configured bounds, it can be constrained, held for a person, or denied; other actions can pass through unchanged. Glacis says it operates nine production components as a managed service alongside the identity provider, cloud, and security systems a customer already uses. Records use OVERT, a published open format that implementers and reviewers can inspect, and can be checked in a browser without a Glacis account. The site says payloads remain in the customer’s environment and records contain hashes; omitting protected fields depends on deployment setup. A record check does not establish whether an action was correct, whether every event was captured, or whether a system is safe or compliant. GLACIS is available by API, web, and self-hosted deployment. Its Free account costs nothing, with no card required. The contact page describes a six-week evaluation for a workflow under review.