T09 · Insecure Skill Coding Practices
Warning
- Location
afm_secm_correlation.py:103- Finding
Silent Substitution of Fabricated AFM Measurement Data
- Content
View full analysis
- Remediation
View remediation
Security audit
Afm Secm Correlation Tools 1.0.0
Security checks for vulnerabilities and agentic risk
Overview
The skill is a local scientific analysis tool, but it can silently replace failed AFM or SECM parsing with synthetic data and produce reports as if they came from the user's experiment.
Install only if you are comfortable reviewing or modifying the parser behavior first. For real research data, malformed or unsupported AFM/SECM files should fail clearly; this version may instead generate realistic-looking synthetic results without telling you.
Vulnerability Patterns
- Insecure Skill Coding PracticesFinds exploitable flaws such as hardcoded secrets or command injection
- 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)
T09 · Insecure Skill Coding Practices
Warning
- Location
afm_secm_correlation.py:189- Finding
Broad Exception Handling Silently Replaces Invalid SECM Input with Synthetic Data
- Content
View full analysis
- Remediation
View remediation
Vulnerability Patterns
- Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
- MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
- Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
- Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
- Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Findings (4)
Ae4
Medium
- Category
- analysis-evasion
- Confidence
- 80% confidence
- Finding
Suspicious Unicode normalization or mixed-script content
- Content
Ae4
Medium
- Category
- analysis-evasion
- Confidence
- 80% confidence
- Finding
Suspicious Unicode normalization or mixed-script content
- Content
Intent-Code Divergence
Medium
- Category
- Not specified by scanner
- Confidence
- 98% confidence
- Finding
When AFM parsing fails or yields insufficient data, the code silently generates synthetic topography and continues analysis as though it were real input. In a scientific analysis skill, this undermines data integrity, can produce fabricated conclusions and reports, and may conceal upstream file corruption or parser failure from the user.
- Content
Intent-Code Divergence
Medium
- Category
- Not specified by scanner
- Confidence
- 99% confidence
- Finding
The SECM loader falls back to a synthetic activity map on parse failure, silently replacing user-supplied experimental data with fabricated values. Because the tool's purpose is structure-activity correlation for research, this can mislead users into trusting false electrochemical trends, taint downstream reports, and mask data ingestion problems.
- Content
Static analysis
No suspicious patterns detected.