Corridor tells your AI assistant how to write secure code.
Acutis checks whether it actually did.
| Acutis | Corridor | |
|---|---|---|
| Approach | Formal verification via property lattice | Advisory context injection |
| When it acts | After code is generated, before it enters your codebase | Before code is generated (injects guidelines) |
| What it checks | Actual code — parsed, analyzed, property flows verified | No code analysis at generation time — PR reviews analyze code post-commit |
| Trust model | Zero trust — unknown = dangerous, missing info = BLOCK | Trusts the AI to follow injected guidelines |
| Failure mode | Over-blocking (safe) | AI ignores guidance (unsafe) |
| Verdict | ALLOW / BLOCK with property flow traces | No verdict at generation time — PR reviews produce findings post-commit |
| Detection guarantee | 100% on CVEFixes benchmark (F1 = 1.0) | No published detection benchmarks |
| Rule maintenance | Zero enumeration — no function lists or patterns | Codebase-specific guardrail configuration |
| MCP integration | Yes | Yes |
| PR reviews | Coming soon | Yes |
| Speed | 0.034ms per scan | Cloud API call latency |
Corridor's approach has a fundamental gap: there's nothing stopping the AI from generating vulnerable code anyway.
Injecting "don't write XSS" into an AI's context is like putting a "wash hands" sign in a kitchen. Good practice. Not enforcement. The AI can — and does — produce vulnerable code despite being told not to.
Corridor's analyzePlan tool runs before code generation. It never sees the actual code. If the AI ignores the guidelines, nothing catches it at the point of generation.
The whole reason you need AI code security is that AI produces unpredictable output. Building security on "the AI will follow instructions" assumes the problem away.
In a controlled study with 40 real coding prompts, AI coding assistants produced vulnerable code 22.5% of the time without formal verification. With Acutis in the loop, that dropped to 0% (McNemar's p = 0.004).
No pre-generation context injection. The AI writes code from your prompt however it wants.
The AI identifies sources, sinks, and transforms in its own code — building a security contract (PCST).
The property lattice traces taint flow from sources through transforms to sinks. If dangerous properties reach a boundary unchecked: BLOCK.
On BLOCK, the AI gets detailed property flow traces and remediation guidance. It fixes the code and resubmits. The loop continues until the code is provably safe.
To be fair.
Corridor ships with SSO, MDM, team dashboards, and organization-wide observability. Acutis is focused on the core verification engine.
Corridor automatically reviews every pull request on GitHub. Acutis verifies at the point of generation — PR-level review is on the roadmap.
Corridor's guardrails can cover policy and best practices beyond specific CWEs. Acutis covers 10 of the 2025 CWE Top 25 (all taint-flow CWEs applicable to Python and JavaScript), with more CWEs coming.
Corridor raised $25M at a $200M valuation (March 2026) with angels from Anthropic, OpenAI, and Cursor. Their team includes Alex Stamos (ex-CSO Facebook) as CPO.
Formal verification is the seatbelt for AI-generated code.