T08 · Insecure Dependencies
Warning
- Location
SKILL.md:924- Finding
Unpinned Third-Party Package Installation Creates a Supply-Chain Risk
- Content
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- Remediation
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Security audit
Cnv Caller Plotter
Security checks for vulnerabilities and agentic risk
Overview
This skill is a genomics analysis tool that can produce hard-coded CNV results while presenting them as real analysis output.
Review carefully before installing or using. Do not use this skill for research, clinical, publication, or patient-related decisions until the CNV algorithm, input validation, plotting, dependency specification, and privacy handling are implemented and independently validated. Treat any current output as demonstration data only.
Vulnerability Patterns
- Insecure DependenciesIntroduces malicious components through unsafe dependency sources
- Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
- Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
- Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
- Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
Findings (2)
other
Error
- Location
scripts/main.py:18- Finding
Program Reports Fabricated CNV Results Without Reading or Validating Inputs
- Content
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- Remediation
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Vulnerability Patterns
- MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
- Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
- Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
- Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
- Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Findings (5)
Tp4
High
- Category
- MCP Tool Poisoning
- Confidence
- 97% confidence
- Finding
The skill markets itself as performing CNV calling, segmentation, and publication-quality plotting, while the document itself later admits the implementation is placeholder and planned improvements still include implementing the actual algorithm. In a genomics workflow, this can mislead users into making research or clinical-adjacent decisions based on fabricated or unsupported results, which is a serious integrity and safety risk even without classic code-execution abuse.
- Content
Missing User Warnings
Medium
- Category
- Not specified by scanner
- Confidence
- 92% confidence
- Finding
The skill processes and writes results derived from whole-genome data, which can contain highly sensitive patient information, but it does not give a clear operational warning near usage/output sections about handling, storage, sharing, and retention of those outputs. In this context, missing privacy guidance increases the likelihood of accidental exposure of genomic data through local files, reports, or downstream sharing.
- Content
Natural-Language Policy Violations
Low
- Category
- Not specified by scanner
- Confidence
- 83% confidence
- Finding
The document uses Chinese labels in the 'Related Skills' section ('上游', '下游') while the rest of the skill is written in English, with no indication that multilingual output is intentional or user-selectable. This can violate language/locale policy when a specific language is imposed without opt-in or justification.
- Content
Intent-Code Divergence
Low
- Category
- Not specified by scanner
- Confidence
- 90% confidence
- Finding
The security checklist claims input validation, output restriction, and sanitized errors, yet the same document states the implementation is placeholder and key functionality is not actually implemented. Security assertions without evidence can cause operators to trust the skill with sensitive genomic inputs under false assumptions, increasing the chance of improper data handling or deployment in regulated contexts.
- Content
Intent-Code Divergence
Low
- Category
- Not specified by scanner
- Confidence
- 97% confidence
- Finding
The prerequisites state that no additional packages are required, but the troubleshooting section names external dependencies such as pysam, numpy, matplotlib, and pandas. This inconsistency is not a direct exploit primitive, but it can cause users to run ad hoc installation commands, weaken reproducibility, and undermine trust in the environment assumptions for a tool handling sensitive genomic data.
- Content