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Security audit

yzy2

Security checks for vulnerabilities and agentic risk

Overview

This skill performs the advertised PDF metadata extraction, but it quietly sends up to 5,000 characters from the PDF to Moonshot's external AI API when an API key is present.

Review before installing or using this skill. Do not run it on confidential, unpublished, regulated, or license-restricted PDFs unless sending the first part of the document to Moonshot is allowed in your environment. Prefer a version that documents the remote data flow, asks for consent, pins dependencies, and offers a local-only mode.

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 (3)

other

Error
Location
extract_title_keywords_abstract.py:15
Finding

PDF Content Is Transmitted to an External AI Service Without Explicit Disclosure

Content
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Vulnerability Details

File Location: extract_title_keywords_abstract.py, lines 15-53
Vulnerability Type: Undisclosed external data transmission
Risk Level: High

Vulnerable Code

python
def extract_metadata_with_ai(text: str) -> dict:
    api_key = os.getenv("MOONSHOT_API_KEY")
    if not api_key:
        return {
            "title_apa": "No API key",
            "keywords": [],
            "abstract": "Please configure the MOONSHOT_API_KEY environment variable"
        }

    client = OpenAI(
        api_key=api_key,
        base_url="https://api.moonshot.cn/v1"
    )

    prompt = f"""
You are an academic paper information extraction assistant. Return only JSON and no other content.

Extract three fields:
1. title_apa: paper title
2. keywords: keyword array
3. abstract: abstract

Paper text:
{text[:5000]}
"""

    try:
        response = client.chat.completions.create(
            model="moonshot-v1-8k",
            messages=[{"role": "user", "content": prompt}],
            temperature=0
        )
        content = response.choices[0].message.content.strip()
        return json.loads(content)
    except Exception:
        return {
            "title_apa": "Extraction failed",
            "keywords": [],
            "abstract": "AI invocation failed"
        }

The displayed English strings above correspond to the original Chinese user-facing messages; the executable behavior and data flow are unchanged.

Technical Analysis

The function extracts up to 5,000 characters from the supplied PDF and places them in a request sent to https://api.moonshot.cn/v1. The skill documentation describes PDF metadata extraction but does not disclose that document contents are transmitted to an external service.

PDF text may include unpublished research, personal information, trade secrets, contractual data, or other confidential material. Truncating the ...[truncated 1463 chars]

Remediation
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Remediation Suggestions

  1. Clearly document that PDF content is sent to Moonshot, including the destination, maximum transmitted size, and applicable retention or privacy considerations.
  2. Require explicit user consent before transmitting document content.
  3. Provide a local-only extraction mode for confidential documents.
  4. Display a warning advising users not to process restricted or sensitive PDFs unless external transmission is authorized.
  5. Minimize transmitted data by first extracting likely title, keyword, and abstract sections locally rather than sending the first 5,000 characters.
  6. Add configuration controls allowing administrators to disable remote processing or approve specific endpoints.
  7. Establish appropriate provider agreements and retention settings where sensitive data processing is expected.

T09 · Insecure Skill Coding Practices

Warning
Location
extract_title_keywords_abstract.py:28
Finding

Untrusted PDF Text Is Embedded Directly in an LLM Instruction

Content
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Vulnerability Details

File Location: extract_title_keywords_abstract.py, lines 28-47
Vulnerability Type: Indirect prompt injection
Risk Level: Medium

Vulnerable Code

python
prompt = f"""
You are an academic paper information extraction assistant. Return only JSON and no other content.

Extract three fields:
1. title_apa: paper title
2. keywords: keyword array
3. abstract: abstract

Paper text:
{text[:5000]}
"""

try:
    response = client.chat.completions.create(
        model="moonshot-v1-8k",
        messages=[{"role": "user", "content": prompt}],
        temperature=0
    )
    content = response.choices[0].message.content.strip()
    return json.loads(content)

The displayed English prompt corresponds to the original Chinese prompt; its structure and executable behavior are unchanged.

Technical Analysis

Text extracted from an untrusted PDF is concatenated into the same user message that contains the extraction instructions. There is no strong separation between trusted instructions and untrusted document content.

A crafted PDF can contain text such as instructions to ignore the extraction task and return attacker-selected values. LLMs do not enforce an inherent security boundary between instructions and data merely because the application labels part of the prompt as paper text.

json.loads() verifies only that the response is syntactically valid JSON. It does not confirm that the returned title, keywords, or abstract accurately represent the document. An attacker can therefore request valid but misleading JSON and still pass the parser.

The current script only prints the parsed result, which limits immediate impact. However, incorrect metadata can propagate into citation systems, document indexes, analytics pipelines, or other consumers of the skill output.

Attack Path

  1. An attacker creates or modifies a PDF to include prompt-like instructions in ext ...[truncated 1018 chars]
Remediation
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Remediation Suggestions

  1. Place fixed extraction rules in a higher-priority system message and identify PDF text explicitly as untrusted data.
  2. Delimit document content clearly and instruct the model never to interpret content inside the delimiters as commands.
  3. Use provider-supported structured output or JSON-schema enforcement rather than relying only on a textual request for JSON.
  4. Validate that the response is an object containing exactly title_apa, keywords, and abstract, with the expected data types and reasonable length limits.
  5. Reject unexpected fields, nested objects, URLs, control text, or anomalous output.
  6. Where practical, use deterministic local parsing to identify title, abstract, and keyword sections before involving an LLM.
  7. Treat generated metadata as untrusted downstream input and escape or validate it before storage, display, indexing, or citation generation.
  8. Add adversarial tests using PDFs containing hidden text and prompt-injection instructions.

T08 · Insecure Dependencies

Warning
Location
SKILL.md:27
Finding

Installation Instructions Use Unpinned Third-Party Dependencies

Content
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Vulnerability Details

File Location: SKILL.md, lines 27-30
Vulnerability Type: Unpinned third-party dependencies
Risk Level: Medium

Vulnerable Code

markdown
## Dependency Installation
Install the dependencies before use:
```bash
pip install pymupdf titlecase openai

The headings and explanatory sentence above are English translations of the original documentation. The installation command is reproduced exactly.

Technical Analysis

The documented installation command does not pin package versions or verify distribution hashes. Each installation can therefore resolve to whatever package versions are current on the configured Python package index and compatible with the environment.

This makes installation non-reproducible and prevents users from confirming that they are running the same reviewed dependency versions. A future compromised release, malicious mirror response, or incompatible update could introduce unintended behavior. Python package installation can execute package build logic, so supply-chain compromise may affect the installation environment before the skill itself runs.

No evidence was found that the named packages are currently malicious, misspelled, or fetched from an explicitly unsafe index. This finding concerns the absence of version and integrity controls rather than a confirmed malicious dependency.

The titlecase package is listed for installation but is not imported by the reviewed Python script, unnecessarily increasing dependency surface.

Attack Path

  1. A user follows the documented pip install command.
  2. pip resolves mutable package versions from the user's configured package index or mirror.
  3. If a selected release or mirror has been compromised, malicious installation or runtime code is downloaded.
  4. Package installation code executes with the privileges of the user running pip.
  5. The compromised dependency may then access data and resou ...[truncated 794 chars]
Remediation
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Remediation Suggestions

  1. Pin all direct dependencies to reviewed versions in a requirements or lock file.
  2. Record cryptographic hashes and install with hash verification, such as pip install --require-hashes -r requirements.txt.
  3. Generate and review a transitive dependency lock using an appropriate dependency-management tool.
  4. Configure trusted package indexes explicitly in controlled deployment environments.
  5. Run dependency vulnerability and provenance scanning in continuous integration.
  6. Upgrade dependencies through a reviewed process rather than resolving arbitrary latest releases during installation.
  7. Remove titlecase unless it is actually required by the implementation.
  8. Avoid installing dependencies with administrator privileges and isolate execution in a virtual environment or restricted container.

SkillSpector

Vulnerability Patterns
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • MCP Least PrivilegeUnderdeclared Capability, Wildcard Permission, Missing Permission Declaration
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
Findings (8)

Tp4

High
Category
MCP Tool Poisoning
Confidence
97% confidence
Finding

The declared behavior says the skill extracts metadata from a local PDF, but the analysis indicates the implementation also sends paper content to an external third-party API and depends on undeclared network access and API credentials. This is dangerous because users may provide unpublished or licensed academic PDFs expecting local-only processing, while the skill could exfiltrate document contents off-host without informed consent.

Content

No source excerpt is available for this finding.

Undeclared Tool Scope

Medium
Category
MCP Least Privilege
Confidence
90% confidence
Finding

The skill invokes a Python script with detected environment access capability but declares no explicit tool scope, permissions, or allowed-tools boundary. In practice, this creates an unnecessary trust gap: the skill may read API keys or other sensitive environment variables without user visibility, and there is no policy-level restriction communicated in the manifest.

Content

No source excerpt is available for this finding.

Description-Behavior Mismatch

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The skill’s stated purpose is PDF metadata extraction, which users would reasonably expect to happen locally. Instead, it sends up to 5000 characters of PDF content to an external AI provider, creating a privacy and data-handling mismatch that can expose sensitive paper contents, unpublished work, or proprietary documents.

Content

No source excerpt is available for this finding.

Context-Inappropriate Capability

Medium
Category
Not specified by scanner
Confidence
92% confidence
Finding

The code depends on an environment-sourced API key and remote API access even though the advertised task is simple PDF metadata extraction. This expands the trust boundary unnecessarily and can surprise operators by causing outbound data flow and credential usage in environments that expected offline processing.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
90% confidence
Finding

User-visible strings and the extraction prompt are written only in Chinese, and the instruction text directs behavior in Chinese without any opt-in or locale selection. This can violate language/locale policy when a skill imposes a specific language on users by default.

Content

No source excerpt is available for this finding.

External Transmission

Medium
Category
Data Exfiltration
Confidence
96% confidence
Finding

The code is hardwired to use an external endpoint, meaning document content leaves the local environment for processing. In the context of a PDF parser skill, this is security-relevant because users may assume local-only handling of potentially sensitive academic documents.

Content

Scanner excerpt · extract_title_keywords_abstract.py (reported line 27)May include surrounding context.

python
client = OpenAI(
        api_key=api_key,
        base_url="https://api.moonshot.cn/v1"
    )

    prompt = f"""

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

The skill transmits extracted PDF text to an external AI service without any user-facing warning, consent step, or disclosure in code behavior. For academic PDFs, this may leak confidential manuscripts, licensed content, or sensitive research data to a third party unexpectedly.

Content

No source excerpt is available for this finding.

Missing User Warnings

Low
Category
Not specified by scanner
Confidence
82% confidence
Finding

The code reads the MOONSHOT_API_KEY environment variable to authenticate with an external service. While this is common, the file provides no comment, docstring, or user-facing explanation that the skill depends on a credential and will use it for outbound requests.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.