Winfunc

AI static analysis that proves exploitability and ships the patch as a pull request

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Screenshot of Winfunc, AI static analysis that proves exploitability and ships the patch as a pull request

What is Winfunc?

Winfunc is a cybersecurity tool. AI SAST scanner that follows attacker-controlled input to the vulnerable line across 25+ languages, attaches CVSS reasoning, traces and proof-of-concept evidence, groups findings into exploit chains and tracks each issue through a validated lifecycle.

Winfunc is an AI-driven static application security testing tool, usually shortened to SAST, that reads a repository and traces attacker-controlled input from its source down to the line where it becomes dangerous. The scanner works across the languages present in a codebase, covering more than 25 of them, from C, C++, Rust, Go and Zig through Java, Kotlin, C#, Python, Ruby, PHP, TypeScript, Swift, Dart, Solidity, Haskell and Elixir. The product positions itself as unbounded by a fixed rule set, meaning findings come from multi-phase analysis of the code rather than from pattern matching against a catalogue of known signatures. Each report is built as evidence rather than a bare alert. Findings carry source-to-sink data-flow traces anchored to lines of code, a CVSS vector with the reasoning behind every severity score, and a confidence score. Where testing falls inside scope, the scanner supplies reproduction steps or proof-of-concept evidence, and it labels observed behavior separately from inferred impact so a reviewer can tell what was actually proven. Authorization, tenant-boundary and business-logic review extend the analysis past classic injection bugs into account takeover paths, permission bypasses and payment-logic flaws, which is where the company's published findings sit. Related issues are grouped into exploit chains, so a low-severity gadget that opens a critical path is reported as part of that path. The tool is aimed at security engineers, AppSec teams and development groups that already run a scanner and are tired of triaging noise. Practically, it audits either a whole revision or only the files that changed, pins every finding to the revision where it appeared, and runs duplicate detection with similarity scoring across scans. A status workflow moves items from pending through validated and accepted to resolved, with validation notes, timestamps and exports keeping the audit trail intact. Filtering by severity, confidence, revision and status supports that triage. Companions products cover automated fixes delivered as pull requests and pull request security checks. Adoption typically starts with a scoped audit of one repository alongside Winfunc's own security engineers, and public findings are available for inspection before any commitment.

How do you use Winfunc?

  1. 1Review the public findings first
    Look through Winfunc's published vulnerability write-ups to judge the depth of analysis and the classes of bug it catches before involving your own code.
  2. 2Scope a first audit
    Book a demo with the security engineering team and nominate a single repository to start with, ideally one with known hard cases.
  3. 3Run the initial scan
    Choose a full revision audit for the first pass so coverage is complete, then switch to incremental scans on changed files as development continues.
  4. 4Triage with traces and scores
    Filter findings by severity, confidence, revision and status, then read the source-to-sink trace, CVSS rationale and proof-of-concept evidence for each item.
  5. 5Move findings through the workflow
    Advance each issue from pending to validated, accepted and resolved, adding validation notes as you go so the audit trail stays complete.
  6. 6Close the loop with Autofix and PR checks
    Evaluate the Autofix and pull request security products to turn confirmed findings into reviewable patches and to catch issues before merge.

Pros and cons

Pros

  • Source-to-sink traces are pinned to specific lines, which cuts the guesswork out of triageAI
  • Observed behavior and inferred impact are labeled separately, so reviewers know what was provenAI
  • Business-logic, authorization and tenant-boundary review goes beyond standard injection checksAI
  • Exploit chains connect low-severity gadgets to the critical paths they enableAI
  • Language coverage spans more than 25 languages, including Rust, Zig, Solidity and ElixirAI

Cons

  • The page lists no integrations, repository hosts or CI hooks, so pipeline fit has to be confirmed in a demoAI
  • No self-serve signup or published pricing; access appears to start with a scoped audit and sales conversationAI
  • Proof-of-concept evidence is only produced where testing is in scope, so some findings arrive as inference onlyAI
  • Confidence scores and separate observed versus inferred labels still require a human reviewer to interpretAI
  • Depth of business-logic reasoning depends on application context you supply, not on code aloneAI

How much does Winfunc cost?

Support

Support is channelled through security engineers: a demo call is the route to scoping a first repository audit, and findings arrive with traces and patches ready for review.

Features

Multi-phase analysis with source-to-sink data-flow tracing anchored to code lines, CVSS vectors with severity rationale, confidence scores, reproduction or proof-of-concept evidence where testing is in scope, authorization and business-logic review, exploit chain grouping, duplicate detection across scans, incremental and full revision scans, and a status workflow from pending to resolved.

Frequently asked questions about Winfunc

  • How do you use Winfunc?
    The walkthrough on this page covers 6 steps: 1. Review the public findings first 2. Scope a first audit 3. Run the initial scan 4. Triage with traces and scores 5. Move findings through the workflow 6. Close the loop with Autofix and PR checks.
  • What platforms does Winfunc support?
    Winfunc is available on Web App.
  • What are the limitations of Winfunc?
    The page lists no integrations, repository hosts or CI hooks, so pipeline fit has to be confirmed in a demo. No self-serve signup or published pricing; access appears to start with a scoped audit and sales conversation. Proof-of-concept evidence is only produced where testing is in scope, so some findings arrive as inference only.

Status

StatusActive
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Added10/21/2026

Platforms

Web App

Pricing

Paid