Akto Agentic Red Teaming vs CyberSecEval
Akto Agentic Red Teaming
akto.ioCyberSecEval
meta-llama.github.ioSide by side
| Figure | Akto Agentic Red Teaming | CyberSecEval |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · AI Security Testing Tools | 8.2 (#9) | 8.8 (#4) |
| Intended users | Akto 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. |
| Purpose | Agentic 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 does | Akto 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
In their own words
Akto Agentic Red Teaming
Akto Agentic Red Teaming has no write-up on Specifiction yet; its figures are above.
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.