Security audit

Quant Tools 1.0.0

Security checks for vulnerabilities and agentic risk

Overview

This is a Chinese-language reference skill for quant research tools with no executable code, though users should be careful before applying its guidance to live trading or hosted services.

Install only if you want Chinese-language quant research guidance. Treat any later use involving broker accounts, API keys, market data services, live trading, cloud deployment, or exposed FastAPI/JupyterHub services as a separate high-impact action requiring explicit confirmation, secrets management, access controls, and legal/compliance review.

A.I.G

Vulnerability Patterns

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
Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs

SkillSpector

Vulnerability Patterns

Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code

Findings (3)

Vague Triggers

Medium

Category: Not specified by scanner
Confidence: 94% confidence
Finding: The trigger list includes very broad, common terms such as '量化', 'quant', '因子', '回测', and '学术研究', which can cause the skill to be invoked in contexts far beyond the author's intended scope. In a skill that discusses data access, backtesting, trading frameworks, and API/service packaging, accidental invocation increases the chance that users are steered into external-data, automation, or trading-adjacent workflows without clear intent.
Content: No source excerpt is available for this finding.

Missing User Warnings

Medium

Category: Not specified by scanner
Confidence: 91% confidence
Finding: The skill prominently describes real-trading frameworks, external data interfaces, JupyterHub, API wrapping, and cloud deployment, but it does not clearly warn that use may involve outbound network access, third-party data transmission, account credentials, or operational actions that could affect live trading environments. In this context, users may underestimate the security and compliance implications of connecting brokerage/data systems or exposing these tools as services.
Content: No source excerpt is available for this finding.

Natural-Language Policy Violations

Low

Category: Not specified by scanner
Confidence: 88% confidence
Finding: The manifest text for the skill description, categories, and several tags is written entirely in Chinese, but there is no indication that the skill is China-region-specific or that users can opt into this locale. Under the policy, forcing a specific language without user choice or documented justification is a natural-language policy concern.
Content: No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.