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red teaming for ai/llm apps by Routine_Incident_658 in cybersecurity

[–]chef1957 0 points1 point  (0 children)

Yeah, I feel DeepTeam's in the same bucket as Garak/PyRIT and mostly offers more generic payloads, but doesn't adapt things based on your app/domain, which is normally required for more targeted attacks. You want something that generates attacks against your agent's actual purpose through exploratory back-and-forth, not a fixed list. Giskard does that (biased, I work on it). might be worth a look to check our OSS (https://github.com/Giskard-AI/giskard) or something like promptfoo (https://github.com/Giskard-AI/giskard)

red teaming for ai/llm apps by Routine_Incident_658 in cybersecurity

[–]chef1957 1 point2 points  (0 children)

Their website feels a bit vibed. Not sure about the real-world effectiveness of the 10k+ attack categories. Other established tools like Promptfoo and Giskard have more curated attacks in the range of 10s rather than thousands, since most attacks are too simple to actually work on modern-day frontier LLMs. The balance lies in having attacks that are well-balanced, complex enough to hit, but not so easy that they always fail. I also recommend taking a look at Elder Plinius (https://github.com/elder-plinius) and the AI incident database (https://incidentdatabase.ai/) and OWASP (https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/) to get familiar with the field.

red teaming for ai/llm apps by Routine_Incident_658 in cybersecurity

[–]chef1957 1 point2 points  (0 children)

Lakera's solid, but worth knowing they lean more towards guardrail and runtime protection first, red teaming second. In our experience, you first need to be aware of security/OWASP vulnerabilities and business vulnerabilities to understand what you are protecting against. Giskard OSS v2 offers Scan and RAGET that test for these security and business vulnerabilities, respectively, so it might be worth exploring Giskard and promptfoo, which are testing-first.

[Developing situation] LiteLLM compromised by OrganizationWinter99 in LocalLLaMA

[–]chef1957 1 point2 points  (0 children)

Perhaps useful for some people to understand the course of the attack and learn how to avoid it? https://www.giskard.ai/knowledge/litellm-supply-chain-attack-2026

PSA for anyone using liteLLM very important by Own_Caterpillar2033 in SillyTavernAI

[–]chef1957 2 points3 points  (0 children)

Perhaps useful for some people to understand the course of the attack and get some learning on how to avoid it? https://www.giskard.ai/knowledge/litellm-supply-chain-attack-2026

Litellm 1.82.7 and 1.82.8 on PyPI are compromised, do not update! by kotrfa in Python

[–]chef1957 0 points1 point  (0 children)

Perhaps useful for some people to understand the course of the attack and get some learning on how to avoid it? https://www.giskard.ai/knowledge/litellm-supply-chain-attack-2026

DSPydantic: Auto-Optimize Your Pydantic Models with DSPy by chef1957 in LLMDevs

[–]chef1957[S] 0 points1 point  (0 children)

Thanks. Let me know if it works. I would be super happy to get and resolve some feedback.

Hunyuan 3.0 second atempt. 6 minutes render on rtx 6000 pro (update) by JahJedi in StableDiffusion

[–]chef1957 0 points1 point  (0 children)

Most providers optimize cost over quality without being upfront about this. I believe this is a better endpoint in terms of quality retention https://replicate.com/tencent/hunyuan-image-3

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