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Security audit

PPT 音频融合演讲稿

Security checks for vulnerabilities and agentic risk

Overview

The skill appears purpose-built for transcription and document generation, but it needs review because it automatically installs unpinned software and handles sensitive meeting files with limited scoping disclosures.

Review before installing. Use it only in a controlled environment or virtual environment, avoid confidential or regulated recordings unless you are comfortable with the local tools and generated files, and approve dependency installation only after pinning or otherwise verifying the packages. Prefer clearer invocation wording and confirm output locations before processing important files.

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
SKILL.md:145
Finding

Automatic Installation of Unpinned Third-Party Dependencies

Content
View full analysis

Vulnerability Details

File Location: SKILL.md, lines 145-152; duplicated at lines 337-344
Vulnerability Type: Supply-chain exposure through automatic, unpinned package installation
Risk Level: Medium

Vulnerable Code

bash
# Check packages one by one and install any that are missing
for pkg in openai-whisper python-pptx Pillow reportlab PyMuPDF python-docx; do
    pip show "$pkg" > /dev/null 2>&1 || pip install "$pkg" 2>&1
done

The surrounding instructions make this environment check mandatory before each execution and direct the Agent to install every missing package automatically.

Technical Analysis

The Skill installs dependencies directly from the environment's configured Python package index without specifying exact versions, verifying cryptographic hashes, using a reviewed lockfile, or requiring user approval. The effective dependency content can therefore change after the Skill itself has been audited.

Python package installation may execute package build or installation logic with the privileges of the Agent process. If a configured package index is compromised, dependency resolution is redirected, or a future dependency release becomes malicious, installation can result in arbitrary local code execution. Installing into the active environment also weakens isolation and makes subsequent behavior non-reproducible.

The listed package names do not appear to be deliberate typosquatting attempts. The risk arises from mandatory installation of mutable, unverified third-party artifacts rather than evidence that any currently named package is malicious.

Attack Path

  1. A user invokes the Skill on a host where at least one listed dependency is absent.
  2. The Skill's mandatory environment check runs pip show for each dependency.
  3. A missing dependency triggers pip install without a pinned version or expected hash.
  4. pip resolves mutable content through the host's configured package index and network path.
  5. A comp ...[truncated 935 chars]
Remediation
View remediation

Remediation Suggestions

  1. Remove automatic package installation from the normal Skill execution path.
  2. Move dependency installation to a separate, explicit setup phase that requires informed user or administrator approval.
  3. Pin every direct and transitive dependency to an audited exact version.
  4. Generate a lockfile containing cryptographic hashes and install with hash enforcement, such as pip install --require-hashes.
  5. Install dependencies inside a dedicated virtual environment or sandbox rather than modifying the Agent's active global environment.
  6. Configure an approved package index and disable untrusted supplemental indexes.
  7. Download and scan dependency artifacts during a controlled build process, then deploy from an internal immutable artifact repository where possible.
  8. Run document conversion and transcription with least privilege and restrict access to unrelated files, secrets, and network resources.
  9. Remove the duplicated installation block at lines 337-344 so that future security changes cannot leave a conflicting legacy copy.
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Findings (11)

Missing User Warnings

High
Category
Not specified by scanner
Confidence
95% confidence
Finding

The skill handles recordings, PDFs, PPTs, and images using external model/tooling but does not clearly warn users that sensitive content may be transmitted to third-party services or processed by external components. This creates a meaningful privacy and compliance risk, especially for confidential meetings, personal data, or regulated business information.

Content

No source excerpt is available for this finding.

Context-Inappropriate Capability

High
Category
Not specified by scanner
Confidence
97% confidence
Finding

The skill instructs the agent to run pip install at execution time, which performs network-backed code acquisition and executes package installation logic in the local environment. This expands the skill from document processing into arbitrary software modification and creates supply-chain and environment-compromise risk if packages are typosquatted, poisoned, or unexpectedly upgraded.

Content

No source excerpt is available for this finding.

Ae4

Medium
Category
analysis-evasion
Confidence
88% confidence
Finding

The file contains a duplicated second block with mojibake/mixed-encoding text, which can obscure the actual instructions reviewers and tools see. Obfuscated or malformed Unicode content increases the chance of hidden behavior, parser inconsistencies, or policy bypass if different components normalize the text differently.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The trigger set includes broad everyday phrases like meeting-note and transcription requests, making accidental activation likely during normal conversation. Unintended activation matters here because the skill can solicit uploads, process sensitive recordings/materials, and initiate local tooling without a deliberate opt-in step.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
94% confidence
Finding

The description explicitly states use of a Chinese transcription engine and the workflow repeatedly requires Chinese transcription/output behavior, but it does not offer language selection or explain a justified region-specific constraint. This is a natural-language locale policy concern because the skill imposes a fixed language without opt-in.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The standalone phrase “帮我整理录音” is a common natural request and is especially prone to accidental matching. In this skill's context, accidental activation could cause privacy-sensitive audio workflows to begin or prompt users to upload files they did not intend to send through this skill.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
86% confidence
Finding

The skill promises generated files and output paths without warning about where files will be written, whether existing files may be overwritten, or how long artifacts persist locally. This can lead to unintentional data exposure on shared machines, namespace collisions, or destructive overwrites of user content.

Content

No source excerpt is available for this finding.

Context-Inappropriate Capability

Medium
Category
Not specified by scanner
Confidence
91% confidence
Finding

The skill invokes system executables such as ffmpeg and soffice, giving it access to host-level tooling outside the narrow LLM workflow. While those tools are plausible for media/document conversion, shelling out increases attack surface, inherits local file permissions, and may process untrusted user files with complex parsers that have a history of security issues.

Content

No source excerpt is available for this finding.

Ae4

Medium
Category
analysis-evasion
Confidence
80% confidence
Finding

Suspicious Unicode normalization or mixed-script content

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
91% confidence
Finding

The trigger phrases are very broad and map to common user intents like transcription and speech recognition, which increases the chance the skill is activated when a user did not specifically intend to invoke it. That can cause unintended processing of sensitive audio, documents, or meeting materials and may route private content into this skill without clear user awareness.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
90% confidence
Finding

The use-case examples reinforce generic invocation language that overlaps heavily with ordinary assistant requests, increasing accidental triggering and misrouting of user content. In a skill handling recordings, meeting notes, PPTs, and PDFs, unintended activation can expose sensitive business or personal data to a workflow the user did not deliberately choose.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.