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Security audit

quant-trading-backtrader

Security checks for vulnerabilities and agentic risk

Overview

This is a coherent Backtrader backtesting skill with ordinary dependency and example-code hygiene risks, not evidence of hidden or malicious behavior.

Install in a dedicated Python virtual environment, prefer pinned requirements or a lockfile, avoid npm install for this package unless the publisher clarifies the JavaScript dependency metadata, and run the example only from a scratch directory where overwriting temp_data.csv would not matter.

A.I.G

Vulnerability Patterns
  • Insecure DependenciesIntroduces malicious components through unsafe dependency sources
  • 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
Findings (2)

T08 · Insecure Dependencies

Warning
Location
package.json:5
Finding

Unpinned and Ecosystem-Inconsistent Third-Party Dependencies

Content
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Remediation
View remediation

T09 · Insecure Skill Coding Practices

Warning
Location
examples/sma_crossover.py:94
Finding

Predictable Temporary Filename Permits File Clobbering

Content
View full analysis
Remediation
View remediation

SkillSpector

Vulnerability Patterns
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
Findings (2)

Unpinned Dependencies

Low
Category
Supply Chain
Confidence
86% confidence
Finding

The dependency uses a caret version range, which allows newer compatible releases to be installed over time. This creates supply-chain risk because builds may become non-reproducible and could unexpectedly pull in a compromised or vulnerable upstream release.

Content

Scanner excerpt · package.json (reported line 6)May include surrounding context.

json
"version": "1.0.0",
  "description": "A skill for building and backtesting quantitative trading strategies using Backtrader.",
  "dependencies": {
    "backtrader": "^1.9.78.123",
    "matplotlib": "^3.0.0"
  },
  "scripts": {

Unpinned Dependencies

Low
Category
Supply Chain
Confidence
90% confidence
Finding

The matplotlib dependency is specified with a broad caret range, so future installs may resolve to different package versions. That increases exposure to accidental breakage or malicious/vulnerable upstream releases in the software supply chain.

Content

Scanner excerpt · package.json (reported line 7)May include surrounding context.

json
"description": "A skill for building and backtesting quantitative trading strategies using Backtrader.",
  "dependencies": {
    "backtrader": "^1.9.78.123",
    "matplotlib": "^3.0.0"
  },
  "scripts": {
    "test": "python examples/sma_crossover.py"

Static analysis

No suspicious patterns detected.