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Audit History

vector-index-tuning - 7 audits

Version comparison

Capability and finding changes across audited versions, newest first.

VersionDateResultReview itemsChange vs previous
v7 LatestJul 8, 2026, 11:55 AM No confirmed findings0No capability change
v6 Jul 8, 2026, 11:55 AM No confirmed findings0No capability change
v5 Jul 1, 2026, 01:11 AM 1 confirmed1External commandsFilesystem access
v4 Jan 17, 2026, 08:42 AM No confirmed findings0No capability change
v3 Jan 17, 2026, 08:42 AM No confirmed findings0No capability change
v2 Jan 4, 2026, 04:40 PM No confirmed findings1No capability change
v1 Jan 4, 2026, 04:40 PM No confirmed findings1Baseline

Jul 8, 2026, 11:55 AM

The static findings are false positives caused by Markdown code fences, reference links, and Qdrant memmap configuration examples. No prompt injection, data exfiltration intent, or unauthorized execution behavior was found in SKILL.md.

1
Files scanned
522
Lines analyzed
3
Review items
0
False positives ignored
Audited by: codex

Jul 8, 2026, 11:55 AM

The static findings are false positives caused by Markdown code fences, reference links, and Qdrant memmap configuration examples. No prompt injection, data exfiltration intent, or unauthorized execution behavior was found in SKILL.md.

1
Files scanned
522
Lines analyzed
3
Review items
0
False positives ignored
Audited by: codex

Jul 1, 2026, 01:11 AM

Static analysis found command, network, filesystem, and weak-crypto patterns, but review shows these are documentation false positives. The skill is a Markdown guide with Python examples for vector index tuning and no evidence of prompt injection, exfiltration, or malicious behavior.

1
Files scanned
522
Lines analyzed
5
Review items
2
False positives ignored

Confirmed security concerns (1)

Low
Weak Cryptography Pattern Has No Cryptographic Context
Verdict: FALSE_POSITIVE. The flagged lines contain the skill description and vector quantization variables such as codes and codebooks. No hashing, encryption, signing, or weak cryptographic algorithm usage was found.
Manual review of the flagged lines found vector compression terminology, not cryptographic operations. No evidence of MD5, SHA-1, DES, RC4, or similar weak algorithms was found.
Capability review items (1)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

Low
Qdrant Memory Mapping Configuration Misidentified as File Access
Verdict: FALSE_POSITIVE. The flagged memmap_threshold values configure when Qdrant may use disk-backed storage. They do not read arbitrary files, write user data, or access local paths.
The lines are Qdrant optimizer configuration values inside a sample collection setup. No filesystem path or direct file operation appears in the reviewed context.
Static false positives ignored (2)

These static matches were dismissed by semantic review or matched schema-only tokens, so they are shown for transparency but do not drive the quality score.

Low
Markdown Code Fences Misidentified as Shell Execution
Verdict: FALSE_POSITIVE. The detected backticks are Markdown code fence delimiters around tables and Python examples, not Ruby or shell backtick execution. No command execution instruction or runtime script was found at these locations.
The flagged lines are visible Markdown fence markers around static documentation. There is no executable Ruby or shell syntax at those lines.
Low
Documentation Links Misidentified as Runtime Network Access
Verdict: FALSE_POSITIVE. The hardcoded URLs are resource links to the HNSW paper, Faiss wiki, and ANN benchmarks. They are not invoked by code and do not send data to external services.
The lines are Markdown references under a Resources heading. No fetch, request, credential handling, or telemetry behavior is present.
Audited by: codex

Jan 17, 2026, 08:42 AM

Pure documentation skill with instructional Python templates for vector index tuning. All static findings are false positives: hardcoded URLs are documentation references, weak crypto patterns matched legitimate quantization terminology, backticks are markdown formatting, and memory-mapped references are Qdrant config parameters.

2
Files scanned
723
Lines analyzed
1
Review items
0
False positives ignored

Risk Factors

🌐 Network access (1)
Audited by: claude

Jan 17, 2026, 08:42 AM

Pure documentation skill with instructional Python templates for vector index tuning. All static findings are false positives: hardcoded URLs are documentation references, weak crypto patterns matched legitimate quantization terminology, backticks are markdown formatting, and memory-mapped references are Qdrant config parameters.

2
Files scanned
723
Lines analyzed
1
Review items
0
False positives ignored

Risk Factors

🌐 Network access (1)
Audited by: claude

Jan 4, 2026, 04:40 PM

Pure documentation skill with instructional Python templates for vector index tuning. Contains Qdrant client examples that make network calls to user-provided database instances - this is expected and legitimate behavior for database tuning content.

4
Files scanned
735
Lines analyzed
2
Review items
0
False positives ignored
Capability review items (1)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

Low
Qdrant client network calls in examples
The Qdrant integration examples import and use QdrantClient which would make network connections to a database. This is legitimate for vector database tuning content. Users provide their own database endpoint.

Risk Factors

🌐 Network access (1)
Audited by: claude

Jan 4, 2026, 04:40 PM

Pure documentation skill with instructional Python templates for vector index tuning. Contains Qdrant client examples that make network calls to user-provided database instances - this is expected and legitimate behavior for database tuning content.

4
Files scanned
735
Lines analyzed
2
Review items
0
False positives ignored
Capability review items (1)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

Low
Qdrant client network calls in examples
The Qdrant integration examples import and use QdrantClient which would make network connections to a database. This is legitimate for vector database tuning content. Users provide their own database endpoint.

Risk Factors

🌐 Network access (1)
Audited by: claude