T08 · Insecure Dependencies
Warning
- Location
SKILL.md:35- Finding
Unpinned Third-Party Dependencies Permit Unsafe Package Resolution
- Content
View full analysis
- Remediation
View remediation
Security audit
Sentiment Analysis
Security checks for vulnerabilities and agentic risk
Overview
This skill should be reviewed because it advertises real stock sentiment analysis but currently returns random simulated financial metrics.
Install only if you understand that the current implementation is a mock-style generator, not reliable stock sentiment analysis. Treat its outputs as test data, use an isolated environment for dependencies, and look for pinned dependencies plus explicit data-source disclosure before using it for real financial decisions.
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 (1)
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 (5)
Natural-Language Policy Violations
Medium
- Category
- Not specified by scanner
- Confidence
- 90% confidence
- Finding
SQP-3 applies to natural-language policy violations in all file types. The document presents the skill instructions and usage entirely in Chinese, which can amount to forcing a specific language without user opt-in, and no explanation is provided that this is a region-specific or Chinese-only tool.
- Content
Intent-Code Divergence
Medium
- Category
- Not specified by scanner
- Confidence
- 97% confidence
- Finding
The module and function claim to perform stock-comment crawling and NLP-based sentiment analysis, but the implementation only generates random values. In a financial-analysis context, this is dangerous because downstream users or agents may treat the output as real market intelligence and make decisions based on fabricated data.
- Content
Natural-Language Policy Violations
Medium
- Category
- Not specified by scanner
- Confidence
- 86% confidence
- Finding
The module title, feature description, and function docstring are presented exclusively in Chinese. Per the policy, forcing a specific language without user opt-in or a documented regional justification is a natural-language policy issue.
- Content
Missing User Warnings
Low
- Category
- Not specified by scanner
- Confidence
- 84% confidence
- Finding
The skill describes analyzing stock-related social media sentiment but does not disclose that doing so may require collecting or transmitting data from external platforms. This creates a transparency and privacy risk because users may unknowingly process third-party content or platform data without understanding the data-handling implications.
- Content
Natural-Language Policy Violations
Low
- Category
- Not specified by scanner
- Confidence
- 93% confidence
- Finding
The description is entirely in Chinese ("情绪分析 - 股吧/雪球评论情感分析") with no indication that the skill supports other languages or that the Chinese-only scope is intentional and region-specific. Under the policy, language constraints should either offer user opt-in/choice or be clearly justified.
- Content