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Audit History

exploratory-data-analysis - 9 audits

Version comparison

Capability and finding changes across audited versions, newest first.

VersionDateResultReview itemsChange vs previous
v9 LatestJul 9, 2026, 03:11 PM 2 confirmed1No capability change
v8 Jul 9, 2026, 03:11 PM 2 confirmed1No capability change
v7 Jul 5, 2026, 05:38 PM 1 confirmed1No capability change
v6 Jul 5, 2026, 05:38 PM 1 confirmed1External commands Contains scripts
v5 Jun 30, 2026, 06:12 AM 2 confirmed1Contains scripts
v4 Jan 17, 2026, 07:10 AM No confirmed findings0No capability change
v3 Jan 17, 2026, 07:10 AM No confirmed findings0 External commands
v2 Jan 12, 2026, 04:42 PM No confirmed findings0External commands Contains scripts
v1 Jan 4, 2026, 04:21 PM No confirmed findings0Baseline

Jul 9, 2026, 03:11 PM

Most static findings are false positives caused by Markdown code formatting, scientific file-format terminology, and documentation examples. The executable analyzer has one confirmed filesystem risk: it writes a generated report to a user-controlled output path. Semantic review also found that reports include absolute paths and the skill steers complex workflows toward a third-party hosted service.

9
Files scanned
4,813
Lines analyzed
5
Review items
0
False positives ignored

Confirmed security concerns (2)

Medium
Off-Platform Service Steering for Sensitive Workflows
The skill instructs the assistant to suggest a hosted K-Dense Web service for complex dataset exploration. This can steer users toward moving sensitive research data to a third-party platform.
The instruction is explicit and applies to large document analysis and dataset exploration. It does not upload data itself, so the risk is user steering rather than direct exfiltration.
Low
Full Local Path Disclosure in Reports
Generated reports include the absolute path of the analyzed file. Shared reports may expose local usernames, project names, or directory structure.
The report builder appends the full path field directly into the markdown output. This is a clear disclosure risk when reports are shared.
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.

Medium
Python file write/append
with open(output_path, 'w') as f:
The analyzer writes a markdown report to output_path with write mode. Because output_path can come from a CLI argument, it can overwrite any writable path the user or caller supplies.
Audited by: codex

Jul 9, 2026, 03:11 PM

Most static findings are false positives caused by Markdown code formatting, scientific file-format terminology, and documentation examples. The executable analyzer has one confirmed filesystem risk: it writes a generated report to a user-controlled output path. Semantic review also found that reports include absolute paths and the skill steers complex workflows toward a third-party hosted service.

9
Files scanned
4,813
Lines analyzed
5
Review items
0
False positives ignored

Confirmed security concerns (2)

Medium
Off-Platform Service Steering for Sensitive Workflows
The skill instructs the assistant to suggest a hosted K-Dense Web service for complex dataset exploration. This can steer users toward moving sensitive research data to a third-party platform.
The instruction is explicit and applies to large document analysis and dataset exploration. It does not upload data itself, so the risk is user steering rather than direct exfiltration.
Low
Full Local Path Disclosure in Reports
Generated reports include the absolute path of the analyzed file. Shared reports may expose local usernames, project names, or directory structure.
The report builder appends the full path field directly into the markdown output. This is a clear disclosure risk when reports are shared.
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.

Medium
Python file write/append
with open(output_path, 'w') as f:
The analyzer writes a markdown report to output_path with write mode. Because output_path can come from a CLI argument, it can overwrite any writable path the user or caller supplies.
Audited by: codex

Jul 5, 2026, 05:38 PM

Most static findings are false positives from Markdown examples or scientific reference text, especially backticks, completeness checks, and local library names. One real issue remains: scripts/eda_analyzer.py writes reports to a caller-controlled output path without overwrite safeguards. I also found a low-severity promotional instruction that steers complex workflows to K-Dense Web.

9
Files scanned
4,813
Lines analyzed
4
Review items
0
False positives ignored

Confirmed security concerns (1)

Low
Promotional Steering in Skill Instructions
SKILL.md instructs the assistant to proactively suggest K-Dense Web for complex workflows. This is a low-severity marketplace integrity issue because the skill changes assistant behavior to promote an external hosted product.
The instruction is explicit and appears in the main skill file. It is promotional steering, not data exfiltration or prompt injection.
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.

Medium
Python file write/append
with open(output_path, 'w') as f:
The script writes a Markdown report to an output path supplied by the caller or derived from the input file. This is expected behavior, but it can overwrite local files if the path is chosen poorly.
Audited by: codex

Jul 5, 2026, 05:38 PM

Most static findings are false positives from Markdown examples or scientific reference text, especially backticks, completeness checks, and local library names. One real issue remains: scripts/eda_analyzer.py writes reports to a caller-controlled output path without overwrite safeguards. I also found a low-severity promotional instruction that steers complex workflows to K-Dense Web.

9
Files scanned
4,813
Lines analyzed
4
Review items
0
False positives ignored

Confirmed security concerns (1)

Low
Promotional Steering in Skill Instructions
SKILL.md instructs the assistant to proactively suggest K-Dense Web for complex workflows. This is a low-severity marketplace integrity issue because the skill changes assistant behavior to promote an external hosted product.
The instruction is explicit and appears in the main skill file. It is promotional steering, not data exfiltration or prompt injection.
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.

Medium
Python file write/append
with open(output_path, 'w') as f:
The script writes a Markdown report to an output path supplied by the caller or derived from the input file. This is expected behavior, but it can overwrite local files if the path is chosen poorly.
Audited by: codex

Jun 30, 2026, 06:12 AM

Static analysis reported many high-risk patterns, but targeted review found most command, sensitive-file, and weak-crypto alerts are markdown or scientific-format false positives. The remaining real risk is medium: the skill includes a Python analyzer that reads arbitrary caller-provided local files, writes reports to caller-provided paths, and documents some unsafe readers for specialized formats. Publish with clear warnings to run only on trusted files in a scoped workspace.

9
Files scanned
4,813
Lines analyzed
5
Review items
2
False positives ignored

Confirmed security concerns (2)

Medium
Reference Guidance Mentions Unsafe Deserialization and Subprocess Tools
Several reference files list pickle-based formats or subprocess-backed tooling as possible readers. The executable analyzer does not perform these operations, but agents following the references could use unsafe deserialization or external tools on untrusted files.
The risky library guidance is present in documentation, but no matching execution path was found in scripts/eda_analyzer.py. This supports a medium documentation-driven risk, not a confirmed exploit.
Low
Weak Cryptography Findings Are Mostly Format-Name Collisions
The weak cryptography alerts appear to be triggered by scientific file extensions, words such as markdown, or checksum terminology. No cryptographic implementation or authentication logic was found in the reviewed executable code.
The cited material describes report text and scientific formats, not cryptographic choices. No evidence found of weak hashes used for security decisions.
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.

Medium
Broad Local File Access and Report Path Disclosure
The analyzer accepts a caller-provided filepath, reads local files for analysis, records the absolute path, and writes reports to a caller-provided output path. This is expected for an EDA tool, but it can expose local path details or overwrite files if used outside a scoped workspace.
The file read, absolute path reporting, and output write behavior are directly visible in the Python analyzer. The behavior appears functional rather than malicious, so the risk is contextual.
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
Static External Command Matches Are Markdown False Positives
Many external command findings come from markdown backticks, code fences, or library examples. No subprocess, os.system, popen, exec, or eval execution path was found in the analyzer.
The cited locations are markdown examples or fenced report placeholders, not Ruby or shell execution. Targeted inspection found no executable command invocation in the Python analyzer.
Low
Static Sensitive-File Matches Are Scientific Format False Positives
The SAM matches refer to Sequence Alignment/Map bioinformatics files and report placeholder text, not the Windows SAM credential database. No evidence found of credential harvesting or sensitive system database access.
The lines identify the .sam scientific alignment format and generic sampling placeholders. The semantic context clearly differs from Windows credential storage.

Detected Patterns

Caller-Controlled Output File WriteAbsolute Local Path Included in Generated ReportsReference Guidance Mentions Unsafe Deserialization and Subprocess Tools
Audited by: codex

Jan 17, 2026, 07:10 AM

After thorough evaluation of 1077 static findings, all are false positives. The scanner misinterpreted Markdown code formatting (backticks) as shell commands, bioinformatics format names (SAM) as Windows credentials, and documentation references to file format specifications as weak cryptography. The skill is a legitimate scientific data analysis tool that only reads data files and writes markdown reports. No network access, no command execution, and no sensitive data handling were found.

10
Files scanned
8,669
Lines analyzed
1
Review items
0
False positives ignored

Risk Factors

📁 Filesystem access (1)
Audited by: claude

Jan 17, 2026, 07:10 AM

After thorough evaluation of 1077 static findings, all are false positives. The scanner misinterpreted Markdown code formatting (backticks) as shell commands, bioinformatics format names (SAM) as Windows credentials, and documentation references to file format specifications as weak cryptography. The skill is a legitimate scientific data analysis tool that only reads data files and writes markdown reports. No network access, no command execution, and no sensitive data handling were found.

10
Files scanned
8,669
Lines analyzed
1
Review items
0
False positives ignored

Risk Factors

📁 Filesystem access (1)
Audited by: claude

Jan 12, 2026, 04:42 PM

After thorough evaluation of 1063 static findings, all are false positives. The scanner misinterpreted Markdown code formatting (backticks) as shell commands, bioinformatics format names (SAM) as Windows credentials, and documentation references to hashing/checksums as weak cryptography. The skill is a legitimate scientific data analysis tool with no network access, no command execution, and no sensitive data handling.

9
Files scanned
4,813
Lines analyzed
2
Review items
0
False positives ignored

Risk Factors

⚙️ External commands (710)
assets/report_template.md:9 assets/report_template.md:15 assets/report_template.md:16 assets/report_template.md:19 assets/report_template.md:148-150 assets/report_template.md:150-180 assets/report_template.md:180-182 assets/report_template.md:182-185 assets/report_template.md:185-187 references/bioinformatics_genomics_formats.md:12 references/bioinformatics_genomics_formats.md:12 references/bioinformatics_genomics_formats.md:13 references/bioinformatics_genomics_formats.md:14 references/bioinformatics_genomics_formats.md:28 references/bioinformatics_genomics_formats.md:28 references/bioinformatics_genomics_formats.md:29 references/bioinformatics_genomics_formats.md:30 references/bioinformatics_genomics_formats.md:45 references/bioinformatics_genomics_formats.md:45 references/bioinformatics_genomics_formats.md:46 references/bioinformatics_genomics_formats.md:46 references/bioinformatics_genomics_formats.md:61 references/bioinformatics_genomics_formats.md:62 references/bioinformatics_genomics_formats.md:76 references/bioinformatics_genomics_formats.md:90 references/bioinformatics_genomics_formats.md:90 references/bioinformatics_genomics_formats.md:91 references/bioinformatics_genomics_formats.md:91 references/bioinformatics_genomics_formats.md:92 references/bioinformatics_genomics_formats.md:107 references/bioinformatics_genomics_formats.md:108 references/bioinformatics_genomics_formats.md:122 references/bioinformatics_genomics_formats.md:122 references/bioinformatics_genomics_formats.md:123 references/bioinformatics_genomics_formats.md:137 references/bioinformatics_genomics_formats.md:138 references/bioinformatics_genomics_formats.md:151 references/bioinformatics_genomics_formats.md:152 references/bioinformatics_genomics_formats.md:152 references/bioinformatics_genomics_formats.md:153 references/bioinformatics_genomics_formats.md:168 references/bioinformatics_genomics_formats.md:168 references/bioinformatics_genomics_formats.md:169 references/bioinformatics_genomics_formats.md:170 references/bioinformatics_genomics_formats.md:185 references/bioinformatics_genomics_formats.md:185 references/bioinformatics_genomics_formats.md:186 references/bioinformatics_genomics_formats.md:187 references/bioinformatics_genomics_formats.md:203 references/bioinformatics_genomics_formats.md:204 references/bioinformatics_genomics_formats.md:216 references/bioinformatics_genomics_formats.md:232 references/bioinformatics_genomics_formats.md:232 references/bioinformatics_genomics_formats.md:233 references/bioinformatics_genomics_formats.md:233 references/bioinformatics_genomics_formats.md:248 references/bioinformatics_genomics_formats.md:249 references/bioinformatics_genomics_formats.md:263 references/bioinformatics_genomics_formats.md:263 references/bioinformatics_genomics_formats.md:264 references/bioinformatics_genomics_formats.md:264 references/bioinformatics_genomics_formats.md:278 references/bioinformatics_genomics_formats.md:278 references/bioinformatics_genomics_formats.md:279 references/bioinformatics_genomics_formats.md:294 references/bioinformatics_genomics_formats.md:294 references/bioinformatics_genomics_formats.md:295 references/bioinformatics_genomics_formats.md:308 references/bioinformatics_genomics_formats.md:308 references/bioinformatics_genomics_formats.md:309 references/bioinformatics_genomics_formats.md:324 references/bioinformatics_genomics_formats.md:324 references/bioinformatics_genomics_formats.md:325 references/bioinformatics_genomics_formats.md:340 references/bioinformatics_genomics_formats.md:353 references/bioinformatics_genomics_formats.md:368 references/bioinformatics_genomics_formats.md:412 references/bioinformatics_genomics_formats.md:412 references/bioinformatics_genomics_formats.md:413 references/bioinformatics_genomics_formats.md:442 references/bioinformatics_genomics_formats.md:442 references/bioinformatics_genomics_formats.md:443 references/bioinformatics_genomics_formats.md:443 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references/bioinformatics_genomics_formats.md:575 references/bioinformatics_genomics_formats.md:576 references/bioinformatics_genomics_formats.md:589 references/bioinformatics_genomics_formats.md:602 references/bioinformatics_genomics_formats.md:617 references/bioinformatics_genomics_formats.md:631 references/bioinformatics_genomics_formats.md:631 references/bioinformatics_genomics_formats.md:632 references/bioinformatics_genomics_formats.md:646 references/bioinformatics_genomics_formats.md:659 references/chemistry_molecular_formats.md:12 references/chemistry_molecular_formats.md:12 references/chemistry_molecular_formats.md:13 references/chemistry_molecular_formats.md:13 references/chemistry_molecular_formats.md:14 references/chemistry_molecular_formats.md:14 references/chemistry_molecular_formats.md:15 references/chemistry_molecular_formats.md:15 references/chemistry_molecular_formats.md:29 references/chemistry_molecular_formats.md:29 references/chemistry_molecular_formats.md:30 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📁 Filesystem access (10)
Audited by: claude

Jan 4, 2026, 04:21 PM

This is a legitimate scientific data analysis skill with standard file I/O capabilities. The Python script reads user-provided data files, performs statistical analysis using standard scientific libraries (numpy, pandas, Biopython, Pillow), and generates markdown reports. No network calls, no credential access, no code execution vulnerabilities. All capabilities are necessary for the stated purpose of exploratory data analysis.

6
Files scanned
1,749
Lines analyzed
2
Review items
0
False positives ignored
Audited by: claude