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CyberSecEval vs Prompt Inspector

CyberSecEval

meta-llama.github.io

Prompt Inspector

promptinspector.io

Side by side

FigureCyberSecEvalPrompt Inspector
Price fromFreeFree
Free planYesYes
Free trial——
Open sourceYesYes
WebYesYes
Windows—Yes
macOSYesYes
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published15
Spec score · LLM Security Tools8.9 (#1)8.5 (#5)
Documentation depthThe 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.
LicenseCybersecurity 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.
PurposeCyberSecEval 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-hostingSelf-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 doesCyberSecEval 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

CyberSecEval

  • CyberSecEval 4Free

CyberSecEval pricing →

Prompt Inspector

  • FreeFree
  • FreeFree
  • EnterprisePriced on request
  • ProPriced on request
  • StarterPriced on request

Prompt Inspector pricing →

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.

Visit meta-llama.github.io

Prompt Inspector

Prompt Inspector has no write-up on Specifiction yet; its figures are above.

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