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aiqbench takeWritten by an AI model; may contain errors — check the source.AI Products

aiqbench analysis

This piece tests Foundry, an evidence-gating layer that sits on top of an LLM vulnerability scanner, against real codebases such as Langflow, MLflow, and PyGoat. The core claim is that governance, not model size, is what makes LLM security output usable: verdicts need citations, repeatable pipelines, and an audit trail. Readers evaluating AI-assisted security tooling should focus on whether the verification step and its results hold up on their own code.
Sources coveringCisco Blogs, Cybersecurity News, CrowdStrike Blog, arXiv
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Foundry in the real world: Turning LLM hunches into verified findings – Security Loop Series, Part 2

Cisco Blogs12 min read

An LLM's "look here" is not "this is real". Foundry adds an evidence gate that turns hunches into verified findings, tested on Langflow, MLflow, and PyGoat.

Part 1 of this series ended with a star. Cisco’s Antares-1B, running on my laptop, points a beam into a repository and says “look here”, and roughly one time in three it’s pointing at the right file. Cheap, private, fast. It also ended with an uncomfortable truth: “look here” is not “this is real”. That gap is not a rounding error. It is the entire distance between a demo and a security program. And it’s not a gap that a bigger model closes on its own. Ask the best LLM you can buy to “find the vulnerabilities in this code” and you’ll get a confident, well-formatted answer. What you won’t get is any way to know which of those claims were checked, whether you’ll get the same answer next Tuesday, or why a given finding exists six months from now when an auditor asks. So this post is about the forge. I took the Foundry security harness specification, a design document Cisco published (not a tool), built a working implementation of it, and then did the thing that matters: I pointed it at real code. Not a toy app with planted bugs. Two large, popular AI/ML platforms, an OWASP training…

Excerpt shown under the publisher's RSS terms · 200 words · full text and figures are on the publisher's site

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