A malformed field. A credential in a code block. A tool response that injects a command. A schema that drifts. These aren't theoretical — they're happening right now in agent systems that look fine in demos.
Send your agent endpoint, repository, or an exported trace log. You get back a concise summary of what an automated surface scan finds — and where a manual Correctover audit goes deeper.
Correctover — AI Reliability
Ready for the full manual audit? I test your agent's output layer across 7 dimensions with 116 detection rules. You get a PDF report with every issue, reproduction steps, and concrete fixes — all async over email, no meetings.
Request the full audit →Every output path that can break downstream parsers — malformed JSON, missing fields, type mismatches, enum drift, nested object errors. Tested across normal, edge-case, and adversarial inputs.
Credential leakage in outputs (AWS/GCP/Azure/SSH tokens in code blocks), prompt injection through tool responses, unsafe content pass-through, command injection vectors. Not keyword matching — semantic intent analysis that traces whether exec() is sandboxed, whether dictionary keys are constant, whether a code path is a legitimate engine or an exploit.
Agent spoofing, unverified tool call sources, missing identity attestation, output tampering, response injection. If your agent can't prove who it is and that its output wasn't modified, it can't be trusted with production systems.
P50/P99 outliers under load, unbounded response times, token explosion on long contexts, loop amplification, budget breach paths. Agents that work in demos can cost 10x expected in production.
Anyone can read a public standard and build a checkbox. What sets this audit apart:
"No source code needed — ship us the published package, we audit it."
Proprietary agent, closed-source MCP server, or a vendor who can't share their repository? The audit runs on what you actually ship and deploy — an npm package tarball, a Docker image, or a compiled binary. Minifiers rename local variables; they don't rename API calls, permission checks, error strings, environment variable names, or URLs. The security semantics survive the build.
Measured on real published artifacts (September 2026 feasibility test, no source code used at any point):
Concrete security mechanisms were reconstructed entirely from published packages: sandbox isolation setup, subprocess command-injection guards, SSRF preflight logic, credential flow and environment handling, and the full MCP protocol state machine — each conclusion backed by extracted code excerpts in the report. Even stripped native modules yield a supply-chain bill of materials from binary strings: dependency versions, toolchain fingerprints, build provenance.
Black-box review covers 80–95% of the JavaScript, configuration, and protocol layers, at roughly 2× the time of source-based review. Deeply native logic in compiled Rust/C++ (beyond strings, symbol tables, and boundary enumeration) sits at 30–40%. Every report states explicitly which conclusions come from the published artifact and which would benefit from supplemental materials — a lockfile, a Docker image, MCP config samples, source maps, or runtime logs. Those are optional enhancers; the audit does not require them to start.
"Scanners flag. Certificates clear."
Enterprises are starting to run automated filtering and allowlists over the skills, plugins, and MCP servers their agents are allowed to install. One vendor reports its platform filters out about 27% of the add-ons and skills it finds online (TechCrunch); researchers measured 17,800+ public AI add-ons across 6.7 million installations pulling instructions from unverified external sources (SiliconAngle). Automated filters flag components by signal — they don't issue archivable evidence that a specific version was independently reviewed.
If your component is blocked or stuck in a customer's allowlist review, a scanner verdict isn't something you can appeal with. Independent third-party audit evidence is. Correctover audits the exact published artifact under our 116-check manual audit methodology — source code not required — and issues a signed CCS receipt:
For component vendors: ship us the published npm package, Docker image, or binary and get a signed receipt your customers' security teams can verify on their own. Request certification for your component →
For enterprise security teams: independent sampling audits plus non-repudiable cryptographic evidence answer the compliance question scanners leave open — who verified the verifier. Ask about independent sampling audits →
Third-party figures per TechCrunch (Sept 1, 2026) and SiliconAngle (Sept 1, 2026). The CCS receipt format follows the Correctover Conformance Shape, published as an individual Internet-Draft — not an RFC, and not an IETF endorsement.
You send sample agent outputs, tool definitions, and expected schemas. Or I test against your staging endpoint.
116 rules across all 7 dimensions, including adversarial inputs, edge cases, and semantic code path analysis.
Full report with findings, fixes, and verification profile.