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Security audit

Ai Intelligent Customer Segmentation

Security checks for vulnerabilities and agentic risk

Overview

The skill describes a legitimate customer segmentation tool, but its install steps run unreviewed code from a mutable remote repository and its customer-data handling is under-specified.

Review the remote repository and pin it to a trusted commit before running the install commands. Use a dedicated virtual environment or container, avoid uploading unnecessary personal data, and confirm consent, retention, access control, and compliance requirements before using it with real customer records.

Vulnerability Patterns
  • Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
  • 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
  • Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
  • Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs
Findings (1)

T03 · Remote Payload Retrieval and Execution

Warning
Location
SKILL.md:40
Finding
Unpinned Remote Payload Retrieval and Execution## Vulnerability Details **File Location**: `SKILL.md`, lines 40-43 **Vulnerability Type**: Remote payload retrieval and execution **Risk Level**: Medium ```bash git clone https://github.com/openclaw-skills/ai-intelligent-customer-segmentation cd ai-intelligent-customer-segmentation pip install -r requirements.txt python app.py ``` ### Technical Analysis The installation instructions direct users to clone a mutable remote Git repository, install the dependencies declared by that repository, and execute its application. The repository is not pinned to a reviewed commit, release tag, or cryptographically verified artifact. Consequently, the code ultimately executed may differ from what existed when this skill file was audited. The project artifact contains only `SKILL.md`; neither `app.py` nor `requirements.txt` is available locally for review. The behavior of the downloaded application and its installation dependencies therefore cannot be verified from the audited artifact. In addition, Python package installation may execute package build or installation logic, extending the risk beyond the explicitly invoked `app.py`. ### Attack Path 1. An attacker compromises the referenced repository, gains control of its organization or maintainer account, or otherwise causes malicious content to appear on its default branch. 2. The attacker modifies `app.py`, `requirements.txt`, or a referenced package so that malicious code is executed during installation or application startup. 3. A user follows the documented commands without pinning or independently reviewing the retrieved revision. 4. `pip install -r requirements.txt` executes dependency installation behavior, and `python app.py` directly executes the downloaded application. 5. The payload runs with the privileges and environmental access of the user executing the commands. ### Impact Assessment Successful exploitation could permit arbitrary code execution under the invok ...[truncated 556 chars]
Remediation
## Remediation Suggestions - Include the complete implementation and dependency manifest in the skill package so they can be audited together. - Pin the Git checkout to a specific reviewed commit hash rather than relying on the mutable default branch. - Publish signed release artifacts and verify their signatures or cryptographic checksums before installation. - Lock every Python dependency and transitive dependency to an exact reviewed version. - Require package hashes during installation, such as through a hash-locked requirements file and `pip --require-hashes`. - Review dependencies for malicious installation hooks, dependency confusion, and typosquatting before distribution. - Execute the application in a dedicated, least-privileged virtual environment or container with restricted filesystem, credential, and network access. - Separate retrieval, verification, dependency installation, and execution into explicit steps so users can inspect the exact payload before running it.
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
Findings (2)

Missing User Warnings

Medium
Confidence
93% confidence
Finding
The skill explicitly targets customer segmentation, profiling, and precision marketing, which commonly involve personal and potentially sensitive behavioral or transaction data. The documentation provides no warning, privacy guidance, consent expectations, or data-handling constraints, increasing the risk that operators will ingest regulated customer data without appropriate safeguards.

Natural-Language Policy Violations

Low
Confidence
92% confidence
Finding
The natural-language description and headings are entirely in Chinese, which can amount to a language-policy issue when no user opt-in or locale limitation is stated. The file does not indicate that the skill is intended only for Chinese-speaking users or provide an alternative language option.

Static analysis

No suspicious patterns detected.