Azure Ai Transcription Py

• Outcome: Pass
• Latest audit: Sep 10, 2026 · 7:19 AM UTC
• Version: 0.1.0

Overview

This is a simple Azure transcription helper; the main considerations are cloud audio sharing and an unpinned pip dependency, not hidden or destructive behavior.

Install in a virtual environment, pin and review the package version where possible, protect the Azure key, and only transcribe recordings or URLs you are allowed to send to Azure, especially if they contain personal, confidential, or regulated speech.

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:12

Finding

Unpinned Third-Party Python Package Installation

Content

View full analysis

Vulnerability Details

File Location: SKILL.md, lines 12-16
Vulnerability Type: Unpinned and unverifiable third-party dependency
Risk Level: Medium

Vulnerable Code

markdown

## Installation

```bash
pip install azure-ai-transcription

text

### Technical Analysis

The installation instruction retrieves the latest version of `azure-ai-transcription` resolved by the user's configured Python package index. It does not pin a reviewed version, verify package hashes, require a lockfile, or explicitly restrict downloads to a trusted index.

Consequently, the installed package contents may change after this Skill has been reviewed. The package itself is not included in the audited project, so its installation and runtime behavior cannot be validated from the available source. If the package release or configured index is compromised, installation may introduce attacker-controlled Python code. Depending on the package format and installer behavior, malicious code could execute during installation, import, or subsequent use.

This finding does not establish that the named package is malicious. The risk arises from instructing users to install an externally controlled and unpinned dependency without integrity verification.

### Attack Path

1. An attacker compromises the relevant package distribution account, package index, dependency resolution path, or a package dependency.
2. The attacker publishes a malicious release that satisfies the unrestricted installation request.
3. A user follows the documented `pip install azure-ai-transcription` instruction.
4. `pip` resolves and installs the attacker-controlled release or compromised transitive dependency.
5. Malicious code executes during installation, import, or use of the transcription client.
6. The code operates with the privileges of the user or automation environment running `pip` and Python.

### Impact Assessment

Successful exploitation could a
...[truncated 635 chars]

Remediation

View remediation

Remediation Suggestions

1. Pin the dependency to an explicitly reviewed version rather than allowing unrestricted latest-version resolution:

   bash

   python -m pip install "azure-ai-transcription==<reviewed-version>"
   

2. Generate and maintain a dependency lockfile that pins both direct and transitive dependencies.
3. Record cryptographic hashes for approved distributions and install with hash enforcement:

   bash

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

4. Configure installation to use a trusted package index or an organization-controlled artifact repository.
5. Verify package ownership, provenance, release signatures or attestations, and the official Azure documentation before recommending the package.
6. Scan pinned package artifacts and transitive dependencies for known vulnerabilities and unexpected installation behavior.
7. Perform installation in an isolated, least-privileged virtual environment rather than as an administrator or root user.
8. Use a controlled upgrade process so new package versions are reviewed and tested before the pin and hashes are updated.

SkillSpector

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep

Findings (2)

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 94% confidence

Finding

The documentation shows users how to submit local audio files and remote audio URLs to Azure for transcription, but it does not clearly warn that this sends potentially sensitive voice content and metadata to a third-party cloud service. In a transcription skill, this omission can lead to accidental disclosure of confidential, regulated, or personal audio data because users may assume processing is local or may not consider the privacy implications of sharing URLs and recordings.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Low

• Category: Not specified by scanner
• Confidence: 84% confidence

Finding

The sample batch and streaming invocations both force locale="en-US", which can imply the skill expects or defaults to a specific language without user opt-in. The file does not explain that other locales may be selected or that en-US is only an example.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.