A scanner matches patterns. It has no idea what your code is trying to do, which is why it misses the broken authorization check and flags the string concatenation that was never reachable. Reading intent is the whole job, and until recently it was the part only a person could do.
CodeSightAI is the engine we built to do that work at scale. It uses a language model's reasoning to understand how data moves through your codebase - what a function intends, where the trust boundaries actually sit, which entry points reach which sinks - and then holds that reasoning to account.
It reviews the AI and LLM code itself, not only the application around it. When it finds a model SDK, prompt files or agent and tool definitions, it runs a dedicated phase against the OWASP Top 10 for LLM Applications and MITRE ATLAS: prompt injection, including through retrieved documents, excessive agency, insecure output handling, secrets in prompts and retrieval isolation. Sending us just an agent or a prompt library? We can run the AI review on its own.
Every finding has to carry written evidence: source location, CWE, OWASP category, a CVSS vector, the MITRE ATT&CK technique it would enable - or, for AI findings, the MITRE ATLAS technique - and a narrative explaining how it would actually be exploited. No finding is marked complete until that evidence exists and passes an audit gate.
When the application can be run, we switch on live validation. CodeSightAI stands up a sealed, network-isolated instance of your own application for the review and replays each finding against it, saving the screenshot and the full request and response as proof. The instance is built when the run starts and torn down the moment it stops.
Where a sealed sandbox is not the right place to prove a finding, the same validation can run against an environment you have authorised us to test - your own staging or production-like target. That path is gated: your written attestation is stored first, then every request is checked against an allowlist of hosts and paths, rate-limited per minute, non-destructive unless you say otherwise, credentialled from encrypted storage and logged in full. It drives a real browser session, so single-page applications, WebSockets and data loaded by POST behave as they do for a real user, and a live feed shows what was allowed and what was blocked.
It runs entirely on our own machines, on a model backend we control, and your source never leaves that boundary. Everything the tool reads from a repository is treated as data rather than instruction, so a file that tries to steer the reviewer gets flagged instead of obeyed.
Today we run CodeSightAI as part of our own engagements rather than selling it. We are weighing up licensing it to teams who want to run it in-house - if that would be useful to you, tell us and it will inform where this goes.
A pattern scanner
Sees syntax.
CodeSightAI
Sees intent - then reproduces the exploit against a live, sealed instance to prove it is real.