AgentScan vs CyberSecEval

AgentScan provides automated security testing for LLMs, RAG pipelines, and autonomous agents through a cloud API and web dashboard. It tests attack patterns including prompt injection, data exfiltration, jailbreaks, and MCP attacks. The product describes 412 vectors across 19 categories, though the Free, Starter, and Pro plans provide smaller subsets. Scan reports include a 0–100 risk score, severity breakdown, remediation advice, and evidence export in JSON or PDF. The dashboard detects agent formats, tracks progress and errors, and lets users adjust throttling. The REST API queues scans, returns a scan ID, and supports completion webhooks. AgentScan maps vulnerabilities to OWASP LLM Top 10 risks and describes links to EU AI Act articles and NIST AI RMF functions; its OWASP mapping excludes Model Denial of Service and Model Theft. Free includes 5 scans per month; paid plans start at $29 a month. The terms restrict scans to agents the customer owns or has explicit written authorization to test.

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.