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Security audit

Uniswap Optimize Lp

Security checks for vulnerabilities and agentic risk

Overview

This is a focused liquidity-strategy skill with no bundled executable code, but users should treat its mutable install commands and model selection as operational cautions.

Before installing, prefer a pinned ClawHub/package version and an immutable Git commit instead of @latest or main. Review any lp-strategist, pool-researcher, or risk-assessor agents separately, and treat the output as financial strategy guidance rather than automatic execution.

A.I.G

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)

T08 · Insecure Dependencies

Warning
Location
README.md:11
Finding

Unpinned Third-Party Packages and Mutable Remote Sources Executed During Installation

Content
View full analysis
Remediation
View remediation

SkillSpector

Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • 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)

Rp1

Medium
Category
MCP Rug Pull
Confidence
95% confidence
Finding

The README instructs users to run npx skills add ... without pinning an exact package version. npx will fetch and execute the latest published package, so a compromised or malicious future release could run arbitrary code on the user's machine during installation. Because this is an installation step for a developer tool, the exposure is real even though it appears in documentation rather than executable code.

Content

No source excerpt is available for this finding.

Rp1

Medium
Category
MCP Rug Pull
Confidence
96% confidence
Finding

The README also recommends npx clawhub@latest install optimize-lp, which explicitly tracks the latest release rather than a fixed, reviewed version. This creates a supply-chain risk: if the package is hijacked or a malicious update is published, users following the documented install command may execute attacker-controlled code. The skill context increases concern because the package installs tooling into an agent environment that may have access to local files, credentials, or repositories.

Content

No source excerpt is available for this finding.

External Model or Provider Selection

Medium
Category
Excessive Agency
Confidence
90% confidence
Finding

Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.

Content

Scanner excerpt · SKILL.md (reported line 4)May include surrounding context.

md
---
name: optimize-lp
description: Get the optimal LP strategy for a token pair — recommends version (V2/V3/V4), fee tier, range width, and rebalance approach based on pair characteristics, historical data, and risk tolerance. Use when the user asks how to LP, what range to use, or which version/fee tier is best.
model: opus
allowed-tools: [Task(subagent_type:lp-strategist)]
---

Static analysis

No suspicious patterns detected.