CyberSecEval vs GuardionAI
CyberSecEval
meta-llama.github.ioGuardionAI
guardion.aiSide by side
| Figure | CyberSecEval | GuardionAI |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | Yes |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 2 |
| Spec score · LLM Security Tools | 8.9 (#1) | 8.7 (#3) |
| Documentation depth | The repository documents benchmark commands, provider formats, self-hosted integration, datasets, judging stages and developer tests. | Public documentation includes API reference pages, cURL examples, detector descriptions, policy behavior, telemetry schemas and DLP integration guidance. |
| What it does | CyberSecEval is Meta’s benchmark suite for measuring cybersecurity risks and defensive capabilities of large language models. | GuardionAI provides runtime visibility, policy enforcement and evidence for customer-facing, autonomous and coding AI agents. |
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.
GuardionAI
GuardionAI provides runtime security and governance for customer-facing AI, autonomous agents, and coding agents. Its gateway evaluates agent and MCP calls inline and can allow, block, or redact actions before they execute. Data protection features identify personal information and secrets, mask or vault-tokenize them before requests leave an organization, and can restore responses for users. Runtime guardrails detect prompt injection, jailbreaks, harmful content, and unsafe tool calls; they can block, redact, or flag actions. A coding-agent plugin monitors tool calls and can stop destructive commands, secret exfiltration, and unauthorized calls before they run. GuardionAI can operate as an inline gateway or through its API, and the maker says this requires no application rewrites. Listed integrations include OpenAI, Claude, Gemini, LangChain, LlamaIndex, LangGraph, Pinecone, AWS, Google Cloud, and Microsoft Azure. The company says its gateway is encrypted, model providers do not see raw data, and customer data is not used to train its models. It describes the service as GDPR-, HIPAA-, and LGPD-ready, with SOC 2 Type II in progress.