Crucible vs CyberSecEval

Crucible is a self-hosted security scanner for testing AI agents before production. It checks attack behavior such as prompt injection, goal hijacking, jailbreaks, unsafe output, and tool misuse. Instead of reviewing generated text alone, it evaluates tool calls, side effects, and outcomes. The site lists 11 modules and 153 attack vectors, covering eight of ten OWASP Agentic risks, with two in development. Crucible runs 90 attack payloads in under 60 seconds and produces an A–F report with severity, OWASP mapping, and reproduction steps. It supports LangChain, CrewAI, AutoGen, raw MCP endpoints, and agents with HTTP or MCP interfaces. The pip-installed CLI supports Python 3.10+. Local scanning needs no signup and has no telemetry by default. Community is free; Team and Enterprise add monitoring, dashboards, support, SSO, audit logs, deployment options, and custom modules.

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.