CyberSecEval vs PromptGuard
CyberSecEval
meta-llama.github.ioPromptGuard
promptguard.coSide by side
| Figure | CyberSecEval | PromptGuard |
|---|---|---|
| Price from | Free | $19/mo |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 8 |
| Spec score · LLM Security Tools | 8.9 (#1) | 8.9 (#2) |
| Spec score · AI Guardrail Software | 9.0 (#1) | 8.7 (#2) |
| Spec score · AI Security Testing Tools | 8.8 (#4) | 8.9 (#3) |
| What it does | CyberSecEval is Meta’s benchmark suite for measuring cybersecurity risks and defensive capabilities of large language models. | PromptGuard places a security gateway before an LLM provider to block prompt injection, jailbreaks, data exfiltration and PII leaks before model processing. |
Plans
PromptGuard
- FreeFree
- FreeFree
- Team$19 a month
- Team$19 a month
- Build$99 a month
- Pro$99 a month
- EnterprisePriced on request
- ScalePrice not published
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.
PromptGuard
PromptGuard is a security layer between an application and its LLM provider, inspecting requests before they reach the model. It describes 15 detectors across six layers for risks such as prompt injection, jailbreaks, PII, data exfiltration, toxicity, fraud, secrets, malware and tool injection. The product says it detects and redacts 43 PII types using reversible tokenization. Its SDK can automatically instrument supported LLM calls with one initialization call while leaving existing provider code unchanged. Listed providers include OpenAI, Anthropic, Gemini, Azure, Bedrock, Mistral, Cohere, DeepSeek, Groq, HuggingFace, Ollama and vLLM. Deployment options include managed cloud, hybrid self-hosting and air-gapped installations. In zero-retention mode, prompt and response content is not stored; default security events include a shortened preview and content hash. PromptGuard says customer prompts, completions and documents are not used to train, fine-tune or evaluate models. The Free tier is permanent and includes 20,000 scans a month, one API key, one project and 24-hour log retention. Paid plans include a 14-day money-back guarantee, not a trial.