CyberSecEval

CyberSecEval is a free benchmark suite for teams assessing cybersecurity behavior in large language models, especially those building with open generative AI. Its range of offensive-risk and defensive-reasoning tests is a strong fit for model evaluation; it is not a runtime guardrail, and AutoPatchBench carries steep hardware demands.

Overview

CyberSecEval 4 measures both vulnerable behavior and defensive capability across several distinct benchmark areas. The PurpleLlama repository licenses the benchmarks under MIT for research and commercial use, making them usable without a benchmark subscription. The project is intended to help the community build responsibly with open generative AI models. In practice, this is an evaluation toolkit for teams that want to examine model behavior, not a service that intercepts or blocks risky requests in production.

Key features

Risky requests, refusals and secure code

MITRE tests measure whether models comply with cyberattack requests, while False Refusal Rate tests look for refusals of borderline benign prompts. That pairing is useful for teams concerned about both unsafe compliance and models declining legitimate work. Secure code tests examine insecure suggestions in instruction and autocomplete settings, with a detector evaluating the responses.

Prompt injection and code execution

Prompt-injection benchmarks cover textual, multilingual textual and visual attacks. A judge LLM evaluates whether the injected instructions succeed, giving evaluators coverage across more than one attack format. The code interpreter benchmark tests compliance with malicious prompts and sorts judged responses into extremely malicious, potentially malicious or non-malicious categories. These tests help teams inspect different routes to unsafe behavior rather than relying on one broad safety measure.

Patch generation and defensive reasoning

AutoPatchBench evaluates AI patch-generation agents on C/C++ vulnerabilities discovered by fuzzing. It requires Podman and substantial compute and storage; the project recommends at least 80 CPUs and 8 TB of storage, and Apple Silicon MacBooks are not currently supported. This makes the patch benchmark a poor fit for modest local machines. CyberSOCEval, developed with CrowdStrike, adds defensive benchmarks for malware analysis and threat intelligence reasoning, broadening the suite beyond model misuse tests.

Running the benchmarks

The getting-started guide describes creating a Python virtual environment, installing requirements and running benchmarks through a Python command-line module. It documents API support for OpenAI, Anyscale and Together, and explains how to add custom provider support for self-hosted models. This setup favors technical teams able to manage their own environment over users seeking a hosted interface.

Pricing

The free plan removes subscription cost and the MIT license permits commercial as well as research use. It remains a self-managed toolkit: teams need to handle setup and, for AutoPatchBench, provide substantial infrastructure.

Platforms

CyberSecEval supports API use, Linux and self-hosted deployment. Its documented Python command-line workflow and custom-provider instructions suit teams integrating evaluations into their own model setup. AutoPatchBench's stated hardware requirements make Apple Silicon MacBooks unsuitable for that benchmark.

Who it's for

CyberSecEval is best for developers and evaluation teams that need to assess cyberattack compliance, refusal behavior, code safety, prompt injection or defensive security reasoning across models. It is particularly relevant when teams want a free, commercially usable benchmark suite and can run Python-based evaluations themselves. Organizations seeking an out-of-the-box production guardrail, or teams without the infrastructure for AutoPatchBench, should look elsewhere or use only the benchmarks their environment can support.

Pros and cons

• Broad evaluation coverage: separate tests address cyberattack compliance, false refusals, insecure code, prompt injection, malicious code-interpreter prompts and SOC reasoning.
• Free for commercial and research use: the open-source suite carries an MIT license, so evaluation does not require a paid benchmark plan.
• Self-hosted model flexibility: the guide supports several API providers and describes adding custom support for self-hosted models.
• Technical setup required: the virtual-environment, requirements-installation and command-line workflow is aimed at teams comfortable running Python tools.
• AutoPatchBench is resource intensive: its recommendation of at least 80 CPUs and 8 TB of storage, plus lack of Apple Silicon MacBook support, rules out many small or local setups.
• Evaluation rather than enforcement: the suite measures model behavior but is not a production control that blocks unsafe outputs.

Alternatives

For a managed guardrail or a different security workflow, consider these options:

• Prompt Inspector is worth comparing if a free allowance of 1,000 detections per month and REST API, SDK and MCP access better fits the intended workflow.
• PromptGuard may suit teams wanting a free service with 20,000 monthly scans, one API key, one project and 24-hour log retention; its Team plan is $19 a month.
• NVIDIA NeMo Guardrails is a free open-source Python package to consider when a guardrails library is a better fit than a benchmark suite; external models and services may need separate setup.
• Amazon Bedrock Guardrails is a paid web option for readers seeking content filters or denied-topic controls rather than model-evaluation benchmarks.
• Openlayer Guardrails offers a free Basic plan with one member, five projects, 20,000 inference logs per month and 20 tests per project, making it an option for teams seeking a project-based guardrails service.
• GLACIS has a free account and may be worth considering for teams seeking a different API, self-hosted or web security tool.
• LLM Guard is another free, self-hosted option for API and Linux environments, though its repository is archived and no longer actively maintained.
• Sanctum Runtime is an MIT-licensed, open-core alternative for readers who want to explore a different runtime-focused product.

Browse LLM Security Tools, AI Guardrail Software or AI Security Testing Tools for related options.

Verdict

Choose CyberSecEval if your team needs a free, commercially usable way to evaluate multiple cybersecurity risks and defensive capabilities in large language models, and can manage the Python-based setup. Its breadth is the main reason to use it; look elsewhere if you need a production guardrail or expect to run AutoPatchBench without substantial compute and storage.