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Security audit

Ai Intelligent Live Chat

Security checks for vulnerabilities and agentic risk

Overview

The skill describes a plausible AI live-chat app, but its install steps run unreviewed code from a mutable remote GitHub repository.

Review this skill carefully before installing. Only run the GitHub install commands in an isolated environment after verifying the repository source, pinning a reviewed commit, checking dependencies, and deciding how customer chat logs and GPT-bound data will be protected.

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

Error
Location
SKILL.md:43
Finding
Mutable Remote Repository Is Retrieved and Executed Without Integrity Verification## Vulnerability Details **File Location**: `SKILL.md`, lines 43–46 **Vulnerability Type**: Remote payload retrieval and execution **Risk Level**: High ```bash git clone https://github.com/openclaw-skills/ai-intelligent-live-chat cd ai-intelligent-live-chat pip install -r requirements.txt python app.py ``` ### Technical Analysis The installation instructions retrieve a mutable remote repository without pinning it to a reviewed commit, signed tag, or integrity hash. They then install dependencies from the repository's unseen `requirements.txt` and execute its unseen `app.py`. Consequently, the code ultimately executed can differ from the content present when this skill package was audited. The supplied artifact contains only documentation and metadata, so the remote application's source, dependency versions, package installation hooks, and runtime behavior cannot be verified locally. Dependency installation may itself execute package build or installation logic before `app.py` is launched. ### Attack Path 1. An attacker compromises the referenced repository, its maintainer account, or an upstream dependency. 2. The attacker adds a malicious dependency, installation hook, or application payload to the repository. 3. A user follows the documented `git clone` command, receiving the attacker's current repository content. 4. `pip install -r requirements.txt` installs the unverified dependencies and may execute malicious build or installation code. 5. `python app.py` executes the unverified application payload. 6. The payload operates with the permissions and environment access of the user who ran the commands. ### Impact Assessment Successful exploitation can result in arbitrary code execution under the invoking user's account. The effective scope can include reading or modifying files accessible to that account, accessing environment variables and locally available credentials, making network connections, and altering ...[truncated 405 chars]
Remediation
## Remediation Suggestions - Include the complete, auditable implementation within the skill package rather than directing users to execute mutable remote content. - If remote retrieval is necessary, pin the repository to a specific reviewed commit and verify the downloaded content against a trusted cryptographic hash. - Use signed releases or signed commits and document the signature-verification procedure. - Pin every Python dependency to an exact version and use a lock file containing integrity hashes, such as a hash-locked requirements file. - Review dependencies for package-name confusion, compromised maintainers, unsafe source distributions, and installation hooks. - Separate retrieval, dependency installation, and execution into explicit steps so users can inspect the content before running it. - Execute the application in a least-privileged, isolated environment with restricted filesystem access, credentials, environment variables, and outbound network connectivity. - Ensure future releases place the reviewed source and dependency manifest under the same versioned artifact covered by the security audit.
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)

Missing User Warnings

Medium
Confidence
90% confidence
Finding
The skill explicitly advertises chat history retention and GPT integration, which implies user conversations may be stored and transmitted to an AI provider, yet the documentation provides no notice about data collection, retention, sharing, or privacy boundaries. In a customer-support context, users commonly disclose personal, account, or sensitive business information, so the omission increases the risk of unsafe deployment and accidental privacy violations.

Natural-Language Policy Violations

Low
Confidence
82% confidence
Finding
The natural-language content of the skill file is entirely in Chinese, which can impose a language constraint on users without explicit opt-in. The file does not state that the skill is region-specific or provide an alternative language option.

Vague Triggers

Low
Confidence
89% confidence
Finding
The manifest description "AI intelligent ai-intelligent-live-chat" is generic and does not indicate when the skill should be invoked versus when it should not. For manifest files, missing specificity about scope or constraints can lead to unintended activation because there are no domain boundaries or exclusion conditions described.

Static analysis

No suspicious patterns detected.