CyberSecEval vs Prompt Inspector
CyberSecEval
meta-llama.github.ioPrompt Inspector
promptinspector.ioSide by side
| Figure | CyberSecEval | Prompt Inspector |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | Yes |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 5 |
| Spec score · LLM Security Tools | 8.9 (#1) | 8.5 (#5) |
| Documentation depth | The repository documents benchmark commands, provider formats, self-hosted integration, datasets, judging stages and developer tests. | The documentation site covers architecture, REST use, Python and Node SDKs, MCP setup and open-source deployment. |
| License | Cybersecurity evaluations and benchmarks in Purple Llama are licensed under MIT, while included Llama safeguard models use separate Llama Community licenses. | The core repository and documentation identify the open-source edition as AGPL-3.0 licensed. |
| Purpose | CyberSecEval 4 is a benchmark suite for assessing cybersecurity vulnerabilities and defensive capabilities in large language models. | Prompt Inspector provides real-time prompt injection detection, sensitive word filtering, and AI safety guardrails through an API call. |
| Self-hosting | Self-hosted models are supported by extending llm.py with query, chat and system-prompt chat implementations. | The maker offers a self-hosted option for non-commercial use under the AGPL-3.0 license. |
| What it does | CyberSecEval is Meta’s benchmark suite for measuring cybersecurity risks and defensive capabilities of large language models. | Prompt Inspector places a real-time security layer between untrusted input and an LLM application or agent. |
Plans
Prompt Inspector
- FreeFree
- FreeFree
- EnterprisePriced on request
- ProPriced on request
- StarterPriced on request
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.
Prompt Inspector
Prompt Inspector has no write-up on Specifiction yet; its figures are above.