Csv Processor Free

• Outcome: Pass
• Latest audit: Sep 16, 2026 · 10:23 AM UTC
• Version: 1.0.0

Overview

This CSV-processing skill is broadly coherent and disclosed, but users should be careful with file writes, dependency installation, and its overly broad trigger wording.

Install in an isolated Python environment, review output paths before export or split operations, avoid overwriting important files, and treat the skill as a CSV preprocessing helper rather than a general analytics or visualization tool.

A.I.G

Vulnerability Patterns

• Insecure Dependencies: Introduces malicious components through unsafe dependency sources
• Skill Instruction Hijacking: Alters the agent's session goals or safety constraints when the skill loads
• Agent Memory Poisoning: Writes attacker-controlled rules into memory that affect later sessions
• Remote Payload Retrieval and Execution: Fetches external code whose behavior can change after review
• Embedded Malicious Code: Ships malicious scripts inside the skill and executes them locally

Findings (1)

T08 · Insecure Dependencies

Warning

• Location: SKILL.md:125

Finding

Unpinned Third-Party Dependency Installation

Content

View full analysis

Vulnerability Details

File Location: SKILL.md, lines 125–130
Vulnerability Type: T08: Insecure Dependencies
Risk Level: Medium

Vulnerable Code Snippet:

markdown

### Prerequisites (approximately 30 seconds)

1. Confirm that Python 3.8+ is installed
2. Install dependencies:

```bash
pip install pandas chardet

text

The dependency table at lines 295–296 only specifies minimum versions and does not make installation reproducible:

```markdown
| pandas | Third-party library | Required | `pip install pandas` | 1.3+ |
| chardet | Third-party library | Required | `pip install chardet` | 4.0+ |

Technical Analysis

The Skill instructs the Agent to install pandas and chardet without exact version constraints or package integrity hashes. Although these package names and the default PyPI source are not inherently suspicious, the installation command resolves whichever compatible releases and transitive dependencies are available through the environment's configured package index at execution time.

Minimum-version documentation such as 1.3+ or 4.0+ does not constrain the provided pip install command and permits future, unaudited releases. The absence of a lock file and cryptographic hashes also prevents verification that downloaded artifacts are the same artifacts reviewed by the Skill publisher.

Python packages or their dependencies can execute code during installation, import, or normal runtime. Consequently, compromise of a package release, transitive dependency, configured package index, or package-resolution environment could introduce executable code into the Agent environment.

Attack Path

1. An Agent or user follows the prerequisite instructions in SKILL.md.
2. The command pip install pandas chardet is executed with the Agent's current operating-system privileges.
3. pip contacts the configured package index and dynamically resolves current package releases a ...[truncated 1301 chars]

Remediation

View remediation

Remediation Suggestions

1. Pin each direct dependency to an exact, reviewed version using == rather than installing unconstrained latest releases.

2. Generate a lock file that records all transitive dependencies and their exact versions.

3. Require cryptographic hashes for every downloaded distribution, for example:

   bash

   python -m pip install --require-hashes -r requirements.txt
   

4. Obtain packages only from an explicitly configured, trusted package index or an internally controlled artifact repository.

5. Review and refresh pinned dependencies through a controlled update process that includes vulnerability scanning and testing.

6. Install dependencies in an isolated virtual environment or container under a non-privileged account.

7. Avoid running pip with administrator or root privileges.

8. Bundle the missing csv_processor implementation or identify its authenticated, pinned source so users do not guess and install an unrelated similarly named package.

SkillSpector

Vulnerability Patterns

• Trigger Abuse: Overly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
• Behavioral AST: exec() Call, eval() Call, Dynamic Import
• MCP Tool Poisoning: Hidden Instructions, Unicode Deception, Parameter Description Injection
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration

Findings (4)

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 85% confidence

Finding

The skill advertises export and file-splitting behavior while also declaring write and exec capabilities, but it does not prominently warn that execution may create or overwrite files on disk. In an agent context, unclear write semantics can lead to unintended filesystem modifications, data leakage into generated artifacts, or destructive overwrites if output paths are inferred or supplied unsafely.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium

• Category: Not specified by scanner
• Confidence: 95% confidence

Finding

The trigger section says the skill should be used whenever the user needs data analysis, report generation, statistical insights, or data visualization. Those are broad, common requests that extend well beyond CSV cleaning and could cause unintended invocation for unrelated analytics tasks.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium

• Category: Not specified by scanner
• Confidence: 93% confidence

Finding

L119 says the skill should be used when '数据分析、报表生成、统计洞察、数据可视化' are needed, but the rest of the file describes only CSV ingestion, cleaning, merging, splitting, type conversion, and export. This is not merely incomplete wording: it broadens the intended use beyond what the skill actually implements.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Low

• Category: Not specified by scanner
• Confidence: 88% confidence

Finding

L344 instructs users to test network connectivity with ping and check firewalls/proxies, but elsewhere the skill is described as using Python plus local libraries only and explicitly says no extra API key is required. This creates an intent/documentation contradiction by implying network-dependent behavior that the skill does not otherwise claim or justify.

Content

No source excerpt is available for this finding.