Skip to content
SPECIFICTION
Get listed

CyberSecEval vs Straiker Defend AI

CyberSecEval

meta-llama.github.io

Straiker Defend AI

straiker.ai

Side by side

FigureCyberSecEvalStraiker Defend AI
Price fromFree—
Free planYes—
Free trial——
Open sourceYes—
WebYesYes
macOSYes—
LinuxYes—
APIYesYes
Self-hostedYesYes
Plans published11
Spec score · LLM Security Tools8.9 (#1)6.7 (#26)
Intended usersThe project describes itself as tools and evaluations intended to help the community build responsibly with open generative AI models.Straiker says it is built for security-sensitive organizations deploying agents, including financial services, healthcare, technology, media, retail, and ecommerce companies.
PurposeCyberSecEval 4 is a benchmark suite for assessing cybersecurity vulnerabilities and defensive capabilities in large language models.Defend AI provides runtime security for coding, productivity, and custom-built AI agents, detecting and blocking prompt injection, data exfiltration, and agent manipulation.

Plans

CyberSecEval

  • CyberSecEval 4Free

CyberSecEval pricing →

Straiker Defend AI

  • Straiker PlatformPrice not published

Straiker Defend AI 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

Straiker Defend AI

Straiker Defend AI has no write-up on Specifiction yet; its figures are above.

Visit straiker.ai