What we still need to verify : 4 points in this profile are not yet confirmed against vendor documentation.
- Supported language list: unconfirmed, verify against vendor docs
- Integration list beyond Git providers and ticketing: confirm
- Whether a traditional static engine runs alongside the model-based analysis: confirm
- Deployment options for customers who cannot send source to a SaaS: confirm
Treat these points as unconfirmed. They are open items in the catalog's verification queue, and this note stays until each is checked against the vendor's documentation.
What it does
Corgea applies large language models to application source code rather than relying primarily on pattern rules or a compiled code graph. Code is chunked with surrounding context and reviewed by models prompted to look for vulnerability classes, with particular emphasis on flaws that rule-based engines handle poorly: missing authorization checks, logic errors in multi-step workflows, and misuse of an internal API in a way that only makes sense if you understand the intent of the surrounding code. That framing is the product's central claim, and it is a reasonable one, because business logic flaws are exactly where signature matching runs out of road.
The second half of the product is remediation. For findings it reports, and in some configurations for findings imported from other scanners, it generates a patch and presents it as a diff or a pull request. It also uses the same model-based reading to triage existing scanner output, marking findings it assesses as not exploitable in context so that a backlog from a noisy tool can be reduced before humans look at it.
Where it fits
It runs against repositories through Git provider integration and in CI, with results surfaced as pull request comments and tickets. The natural owner is a security engineer who is drowning in findings from an incumbent scanner and wants triage and fixes accelerated. It assumes you are comfortable with source code being processed by a model-backed service, which is a conversation some organizations will need to have before evaluation starts.
Strengths
- Targets business logic and authorization flaws, a category that traditional static analysis genuinely cannot reach.
- Fix generation shortens the distance between a finding and a merged change, which is where most AppSec programs actually lose time.
- Triage of third-party scanner output is a pragmatic entry point that does not require replacing an incumbent tool.
Limitations
- Model-based analysis is non-deterministic. Two runs on the same code can differ, which complicates gating, regression testing and audit evidence.
- Generated patches require human review. They can be subtly wrong, and a confident wrong fix is more dangerous than an open finding.
- Young product with a shorter track record than the established engines, and published detail on coverage and accuracy is limited.
Who it suits
Fits teams with an existing scanner producing more findings than they can process, and an appetite for AI-assisted workflow. It is a poor fit for organizations that need deterministic, reproducible results for compliance, or that cannot permit source code to leave their boundary.
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