Skip to content
SPECIFICTION
Get listed

Akto Agentic Red Teaming vs CyberSecEval

Akto Agentic Red Teaming

akto.io

CyberSecEval

meta-llama.github.io

Side by side

FigureAkto Agentic Red TeamingCyberSecEval
Price from—Free
Free plan—Yes
Free trial——
Open source—Yes
WebYesYes
macOS—Yes
Linux—Yes
APIYesYes
Self-hostedYesYes
Plans published11
Spec score · AI Security Testing Tools8.2 (#9)8.8 (#4)
Intended usersAkto describes its AI security platform as built for enterprise teams and modern AppSec teams.The project describes itself as tools and evaluations intended to help the community build responsibly with open generative AI models.
PurposeAgentic Red Teaming adversarially tests AI agents, copilots, RAG systems, and AI-powered applications for vulnerabilities.CyberSecEval 4 is a benchmark suite for assessing cybersecurity vulnerabilities and defensive capabilities in large language models.
What it doesAkto describes Agentic Red Teaming as continuous adversarial testing for AI agents, copilots, RAG systems, and AI-powered applications.CyberSecEval is Meta’s benchmark suite for measuring cybersecurity risks and defensive capabilities of large language models.

Plans

Akto Agentic Red Teaming

  • Akto ArgusPriced on request

Akto Agentic Red Teaming pricing →

CyberSecEval

  • CyberSecEval 4Free

CyberSecEval pricing →

In their own words

Akto Agentic Red Teaming

Akto Agentic Red Teaming has no write-up on Specifiction yet; its figures are above.

Visit akto.io

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