Skip to content
SPECIFICTION
Get listed

AgentDojo vs CyberSecEval

AgentDojo

agentdojo.spylab.ai

CyberSecEval

meta-llama.github.io

Side by side

FigureAgentDojoCyberSecEval
Price from—Free
Free plan—Yes
Free trial——
Open source—Yes
Web—Yes
macOS—Yes
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published01
Spec score · AI Security Testing Tools7.8 (#12)8.8 (#4)
LicenseThe project's GitHub page identifies its license as MIT.Cybersecurity evaluations and benchmarks in Purple Llama are licensed under MIT, while included Llama safeguard models use separate Llama Community licenses.
Model providersDocumented tool-calling LLM implementations support OpenAI, Anthropic, Google, and Cohere; the docs also describe using Llama 3 70B through TogetherAI’s API.The getting-started guide lists API support for OpenAI, Anyscale, and Together, and describes how to add custom provider support for self-hosted models.
PurposeAgentDojo is a dynamic environment for evaluating prompt injection attacks and defenses for LLM agents.CyberSecEval 4 is a benchmark suite for assessing cybersecurity vulnerabilities and defensive capabilities in large language models.

Plans

AgentDojo

No plans published.

AgentDojo pricing →

CyberSecEval

  • CyberSecEval 4Free

CyberSecEval pricing →

In their own words

AgentDojo

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

Visit agentdojo.spylab.ai

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