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Wrap your existing known-answer-test (KAT) runner in a narrowly scoped MCP tool. The model can then select an approved algorithm and test case, while trusted code validates the request, runs the implementation, compares the result, and returns a structured record. That replaces repeated hex transcription in the workflow; it does not make the runner an official NIST tool or turn a passing test into a certificate.
Why expose a KAT runner as a tool?
Pasting test inputs and expected outputs into a prompt makes the model-facing workflow depend on manually copied data. A tool can instead refer to a pinned vector set and a case identifier, so the trusted runner—not model-authored text—selects the bytes, invokes the implementation, and performs the comparison.
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MCP tools have names, descriptions, and input schemas. A client can discover available tools with tools/list and invoke one with tools/call. The MCP specification describes this model and permits tool availability to vary with request authorization. See the MCP Server Tools specification dated 2026-07-28.
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The useful boundary is simple: the model chooses among capabilities you expose; it does not get a general-purpose shell or permission to invent a command line. MCP does not prescribe a crypto KAT schema, so the names, accepted algorithms, and invocation behavior must match the runner you actually support.
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What should the tool accept and return?
A conceptual tool named run_kat could accept an allowlisted algorithm, a pinned vector-set identifier, a case selector, and a configured implementation target. This is design guidance, not a standard MCP or NIST schema.
{
"algorithm": "AES-GCM",
"vector_set": "approved-set-id",
"case_id": "case-id",
"implementation_target": "configured-target"
}
Validate every field against the runner’s supported values before execution. The response should make the outcome auditable and reproducible without dumping an unbounded transcript:
{
"algorithm": "AES-GCM",
"vector_set": "approved-set-id",
"vector_set_version": "pinned-version",
"case_id": "case-id",
"implementation_build": "build-id",
"comparison": "pass",
"error_category": null
}
Include enough provenance to identify what ran: algorithm, corpus and version, case, implementation or build identifier, comparison status, and a concise error category when relevant. These example field names and values are illustrative, not protocol requirements. Whether to expose expected values is a threat-model decision. When vectors or outputs are large, return the case identifier and bounded diagnostic detail rather than asking the model to handle free-form hex.
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How should a request move through the runner?
- Discover: The MCP client lists available tools and their descriptions and schemas.
- Select: The caller chooses a supported algorithm, pinned vector set, known case, and configured target.
- Validate: The wrapper rejects unknown values and malformed requests against an allowlist; it never treats an argument as an arbitrary command.
- Execute: Trusted runner code loads the selected vector and supplies its inputs to the configured implementation.
- Compare: The runner checks the implementation’s output against the expected answer using the test’s defined comparison rules.
- Report: The tool returns a bounded, machine-readable result with provenance and a useful failure category.
Keep corpus updates explicit and reviewable: pin the version used for a run and make changes to the accepted set visible to maintainers. Keep secrets out of tool arguments, logs, and model-visible results unless they are essential and authorized.
How does this compare with pasting vectors by hand?
This is an engineering comparison, not a measured performance benchmark. The trade-off is between an easy-to-start manual process and the control and repeatability of a maintained integration.
| Concern | Paste hex into a prompt | Call an MCP wrapper |
|---|---|---|
| Transcription exposure | Inputs and expected values are copied into the model-facing workflow, creating opportunities for copy or formatting errors. | A case identifier can select bytes from the runner’s pinned corpus; the model need not transcribe them. |
| Repeatability | Depends on the exact text pasted and the steps followed for each run. | Can be repeatable when the vector set, case, runner, and implementation build are identified. |
| Provenance | May be difficult to reconstruct unless the prompt and test source are separately recorded. | Can be returned as structured metadata alongside the result. |
| Access control | Depends on the surrounding environment and what the operator exposes. | The server can restrict algorithms, corpora, targets, and callers, but these controls must be implemented. |
| Output auditability | Results may be mixed into conversational text and need interpretation. | A defined response can distinguish pass, mismatch, invalid request, and execution error. |
| Setup and maintenance | Requires little integration, but repeated manual handling remains. | Requires a maintained wrapper, schema, corpus pinning, and operational safeguards. |
What security controls belong around the tool?
The MCP tools specification says servers “MUST: Validate all tool inputs; Implement proper access controls; Rate limit tool invocations; Sanitize tool outputs.” It also says, “For trust & safety and security, there SHOULD always be a human in the loop with the ability to deny tool invocations.” The same specification says clients should confirm sensitive operations, show tool inputs, validate results, and use timeouts. These requirements and recommendations apply to the tool boundary; a schema by itself is not an access-control system. See the MCP tools security guidance.
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- Allow only known algorithms, pinned corpora, cases, and configured implementation targets.
- Apply caller authorization, rate limits, and execution timeouts appropriate to the runner.
- Sanitize errors and outputs so they do not reveal secrets, internal paths, or unnecessary input material.
- Show sensitive inputs to the user and support confirmation or denial where the operation warrants it.
- Keep tool results bounded; provide enough detail to diagnose a mismatch without returning sensitive data by default.
What a passing KAT does—and does not—establish
KATs check selected input-output pairs
A known-answer test checks whether an implementation produces the expected result for a known input. A pass is evidence about that execution and those selected vectors, not proof that the implementation is secure, bug-free, compliant, or validated. NIST says its block-cipher response (.rsp) vectors and intermediate files may be used for informal correctness checks, and explicitly warns: “Use of these test vectors does not replace validation obtained through the CAVP.” See NIST CAVP Block Ciphers and Test Vectors.
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The Automated Cryptographic Validation Protocol (ACVP) is a JSON request/response protocol for cryptographic testing. In the NIST workflow, capability information is provided, vectors are generated to match, the implementation runs the inputs, and the outputs are checked by the ACVTS testing system. The protocol models client, server, optional proxy, and device-under-test roles; it does not define the algorithm, the device’s API, or how tests are generated. As the NIST-hosted ACVP JSON Specification states: “ACVP does not define the cryptographic algorithms, nor does it detail the precise conditions for a response to be acceptable.” A local MCP wrapper is not an ACVP client unless it implements that protocol and workflow.
CAVP validation is not the same as a local tool result
NIST describes algorithm validation as a prerequisite to cryptographic module validation. Production ACVTS testing is restricted to NVLAP-accredited testing laboratories; successful testing through the program is the path for certificates listed by CAVP. The implementation is tested as a black box: it is not supplied to NIST. A local MCP tool can help run checks, but it does not itself confer CAVP validation. See the NIST Cryptographic Algorithm Validation Program page for the program’s workflow and scope.
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Complement KATs with adversarial vectors
Known-answer vectors are not the only useful tests. Project Wycheproof provides JSON vectors aimed at known attacks, specification inconsistencies, and implementation bugs. Its documentation recommends loading vectors, mapping them to the implementation’s crypto API, comparing produced outputs with expected outputs, and integrating the tests into CI. It lists coverage for algorithms including AES-GCM, ECDSA, RSA, ML-KEM, and ML-DSA; that coverage is not exhaustive security testing. Wycheproof is community managed, so review and pin the corpus version you adopt. See Project Wycheproof.
A runner can expose a separate allowlisted vector-set choice for this kind of testing, rather than treating all vectors as interchangeable. Preserve each corpus’s identity and expected semantics in the result metadata so a pass against one set is not mistaken for a broader security conclusion.
Quick Recap
Implementation checklist
- Start with a runner that already works independently of MCP, and document its supported algorithms and input behavior.
- Expose a narrow tool with schema-validated selectors instead of arbitrary commands or raw model-authored hex.
- Pin vector-set versions and implementation build identifiers; make corpus changes reviewable.
- Return structured status and concise diagnostics, with expected-value visibility chosen deliberately.
- Implement validation, access controls, rate limits, output sanitization, and timeouts; add human confirmation for sensitive runs where appropriate.
- Use the tool for repeatable testing, while keeping ACVP/ACVTS and formal CAVP validation distinct from local KAT results.
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