kaushik92
Show HN: Compliant LLM toolkit for ensuring compliance & security of AI systems
https://github.com/fiddlecube/compliant-llm
With the right technique, I was able to break the so-called secure models like Claude and OpenAI.So, I built an open-source tool to automate this and find security holes in any hosted model.I got claude-sonnet-4 to demonstrate the following harmful behavior:- steal data from downstream tool calls using sql injection, code injection and template injection attacks- install spyware or malware...
Show HN: Compliant-LLM: Audit AI Agents for Compliance with NIST AI RMF
https://github.com/fiddlecube/compliant-llm
We're excited to launch compliant-llm: an open-source toolkit that helps infosec and compliance teams audit AI agents against regulatory frameworks like NIST AI RMF, ISO 42001, and OWASP Top 10.Infosec and compliance teams are now responsible for tracking security and compliance risks of a growing number of AI agents across external and internal apps and third-party vendors.compliant-llm gives...
Our goal is to focus on datasets and make it very easy to create and manage data.
In our next release, we will be launching a way to do this using a UI.
Apart from this, we generate a diverse set of questions including complex reasoning and chain of thought.
We also generate domain specific unsafe questions - questions that violate TnC of the particular LLM to test the model guardrails.
We are working on getting SOC2 certified. In the meantime, we sign a legally binding agreement with our users who have data privacy needs/concerns.
We incorporate user prompts to generate the outputs and provide diagnostics and feedback for improvement, rather than eval metrics. So you can plug your low scored queries provided by Ragas, your prompt and context. FiddleCube can provide the root cause and the ideal response.
This is an alternative to manual auditing and testing, where an auditor works on curating the ideal dataset.
Show HN: FiddleCube – Generate Q&A to test your LLM
https://github.com/fiddlecube/fiddlecube-sdk
Convert your vector embeddings into a set of questions and their ideal responses. Use this dataset to test your LLM and catch failures caused by prompt or RAG updates.Get started in 3 lines of code:```pip3 install fiddlecube``````from fiddlecube import FiddleCubefc = FiddleCube(api_key="<api-key>") dataset = fc.generate( [ "The cat did not want to be petted.", "The cat...
On a side note - I love how quickly your team is shipping! Do keep it going!