Microsoft GH-300 Bundle

Generated tests copied the implementation's mistakes into expected values. The stated evidence instead indicates: The generated examples were not executed against the library.

The generated suite exercised only the happy path. The stated evidence instead indicates: The generated examples were not executed against the library.

A generated mapping guessed columns and failed on uncommon CSV rows. The stated evidence instead indicates: The generated examples were not executed against the library.

The security review lists each outbound and inbound data exchange and demonstrates a disallowed MCP source cannot be read even if the model can request other approved data. However, this does not establish the required outcome: a controlled completion changes with the surrounding function and comments.

The explanation is limited to verifiable inputs and avoids invented architecture. However, this does not establish the required outcome: a controlled completion changes with the surrounding function and comments.

The resulting technical answer cites text actually obtained through an approved tool or explicitly states that the external URL contents were not accessed. However, this does not establish the required outcome: a controlled completion changes with the surrounding function and comments.

The generation produced custom security logic without cryptographic scrutiny. The stated evidence instead indicates: An AI-suggested debugging step exfiltrated a secret to observable logs.

Only happy-path output was tested for security-sensitive generated logic. The stated evidence instead indicates: An AI-suggested debugging step exfiltrated a secret to observable logs.

An AI-suggested destructive command was treated as safe without validation. The stated evidence instead indicates: An AI-suggested debugging step exfiltrated a secret to observable logs.

Real confidential records were treated as acceptable sample data. The specific problem here is that the generated serializer was reviewed for syntax but not for schema compatibility.

An AI-suggested debugging step exfiltrated a secret to observable logs. The specific problem here is that the generated serializer was reviewed for syntax but not for schema compatibility.

An apparently safe upgrade altered runtime semantics without review. The specific problem here is that the generated serializer was reviewed for syntax but not for schema compatibility.

The required historical decision was absent from the model's active context. The specific problem here is that cursor-adjacent code was ignored when explaining completion relevance.

Unnecessary prompt material diluted the specific syntax question. The specific problem here is that cursor-adjacent code was ignored when explaining completion relevance.

Plausible generated option names were accepted without source checking. The specific problem here is that cursor-adjacent code was ignored when explaining completion relevance.

An authorized administrator verifies the effective scoped exclusion policy and supported user-surface behavior, while independent agent/MCP restrictions are tested separately. However, this does not establish the required outcome: the referenced repository and obligations are reviewed when found, and the absence of a reference is not documented as proof no matching code exists.

After policy propagation a new supported client request does not include the excluded content; existing visible history is not incorrectly described as retroactively erased. However, this does not establish the required outcome: the referenced repository and obligations are reviewed when found, and the absence of a reference is not documented as proof no matching code exists.

The reviewer records that no reference is not proof of originality and checks any relevant external/private/updated source separately before legal signoff. However, this does not establish the required outcome: the referenced repository and obligations are reviewed when found, and the absence of a reference is not documented as proof no matching code exists.

Review expectations were not supplied in an accessible instructions file. The specific problem here is that the generated summary was accepted without comparing it to the actual changes.

The automation token lacked Copilot management authority or used the wrong endpoint. The specific problem here is that the generated summary was accepted without comparing it to the actual changes.

The file was never made available to the model's current context. The specific problem here is that the generated summary was accepted without comparing it to the actual changes.

The model relied on an obsolete API pattern without checking the project version. The specific problem here is that the generation produced custom security logic without cryptographic scrutiny.

Only happy-path output was tested for security-sensitive generated logic. The specific problem here is that the generation produced custom security logic without cryptographic scrutiny.

Potentially matching public code was accepted without reviewing its licensing context. The specific problem here is that the generation produced custom security logic without cryptographic scrutiny.

The suggested output was accepted without examining available references. The specific problem here is that the team treated content exclusions as a universal third-party tool boundary.

Third-party tool authorization exceeded the task's necessary scope. The specific problem here is that the team treated content exclusions as a universal third-party tool boundary.

A broad or misspelled pattern failed to match the intended files. The specific problem here is that the team treated content exclusions as a universal third-party tool boundary.

Copilot Business/Enterprise content exclusions depend on configured paths, client support and feature mode; they are not a general access-control substitute. The two requirements instead call for these controls: (A) Keep code scanning, tests and security review alongside the matching filter; (B) Read applicable GitHub product terms and any available matching-public-code source licenses, record the business's legal acceptance decision and verify provenance independently before redistribution.

Copilot matching-code references are conditional provenance hints, not complete source inventories, automatic attribution, or a legal approval. The two requirements instead call for these controls: (A) Keep code scanning, tests and security review alongside the matching filter; (B) Read applicable GitHub product terms and any available matching-public-code source licenses, record the business's legal acceptance decision and verify provenance independently before redistribution.

Documented inline suggestion exclusions do not automatically apply to VS Code Copilot Edit/Agent mode or every other IDE feature. The two requirements instead call for these controls: (A) Keep code scanning, tests and security review alongside the matching filter; (B) Read applicable GitHub product terms and any available matching-public-code source licenses, record the business's legal acceptance decision and verify provenance independently before redistribution.

A correct-looking completion was treated as proof of functional correctness. The stated evidence instead indicates: Retention and training guarantees were guessed from the product name alone.

The request's effective context lacked a necessary cross-package definition. The stated evidence instead indicates: Retention and training guarantees were guessed from the product name alone.

The team assumed deterministic output for unspecified generative requests. The stated evidence instead indicates: Retention and training guarantees were guessed from the product name alone.