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Security audit

azure-ai-transcription-py-free

Security checks for vulnerabilities and agentic risk

Overview

This is a straightforward Azure transcription guide, with some disclosure and dependency hygiene issues users should review before using it with sensitive audio.

Use this only in an isolated Python environment and pin or review the dependency before installation. Do not submit confidential, regulated, or consent-sensitive audio unless your organization allows Azure cloud processing and you understand how SAS URLs, transcripts, and Azure logs are handled. Rotate and scope Azure keys appropriately.

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:46
Finding

Unpinned Third-Party Package Installation

Content
View full analysis

Vulnerability Details

File Location: SKILL.md, lines 46–49
Vulnerability Type: Unpinned third-party dependency
Risk Level: Medium

Vulnerable Code

markdown
## 安装

```bash
pip install azure-ai-transcription
text

### Technical Analysis

The installation instructions direct the agent or user to install `azure-ai-transcription` without pinning an exact version, verifying package hashes, or restricting retrieval to an explicitly trusted package index.

Consequently, the effective code installed and executed can change after the Skill has been audited. Python package installation can execute package-controlled build logic, and the installed package will later run with the privileges of the user or agent process. A compromised package release, package-name ownership change, or unintended future release could therefore introduce arbitrary code into the environment.

The audit did not establish that the named package is currently malicious. The confirmed issue is the unsafe, non-reproducible dependency installation practice.

### Attack Path

1. An attacker compromises the package publisher account, distribution infrastructure, or a future package release.
2. The attacker publishes a malicious version under the package name referenced by the Skill.
3. A user or agent follows the documented command:
   `pip install azure-ai-transcription`.
4. Pip resolves and downloads the attacker-controlled version because no exact version or hash is required.
5. Malicious installation logic may execute during installation, or malicious runtime code may execute when the package is imported and used.
6. The payload operates with the permissions and accessible environment of the invoking user or agent.

### Impact Assessment

Successful exploitation could result in arbitrary code execution under the account running pip or the transcription workflow. Depending on that account's permissions, the malicious dependency could access local files, environment variabl
...[truncated 306 chars]
Remediation
View remediation

Remediation Suggestions

  1. Pin the dependency to an exact, reviewed version, for example:

    bash
    python -m pip install azure-ai-transcription==<reviewed-version>
    
  2. Generate and verify cryptographic hashes through a locked requirements file:

    text
    azure-ai-transcription==<reviewed-version> \
        --hash=sha256:<verified-package-hash>
    

    Install it using:

    bash
    python -m pip install --require-hashes -r requirements.txt
    
  3. Configure pip to use an explicitly trusted package index or an organization-controlled artifact mirror.

  4. Verify the package name, publisher, release provenance, and expected distribution files before documenting the dependency.

  5. Run dependency installation in an isolated virtual environment or container under a non-privileged account.

  6. Add automated dependency scanning and controlled update review so version changes require security validation before deployment.

Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Findings (4)

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The skill documentation does not clearly warn users that audio URLs and resulting transcription content are transmitted to Azure cloud services for processing. In a transcription context, inputs often contain sensitive voice, personal, or regulated data, so inadequate disclosure can lead users to expose confidential material to third-party processing without informed consent or proper compliance review.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
94% confidence
Finding

L013-L014 与 L187 明确声明该基础版“不含…时间戳字幕生成等高级能力”。但 L128 具体说明如何把每个片段起止时间戳格式化为字幕条目导出,这与前述能力边界直接矛盾,属于文档意图与描述能力不一致。

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
92% confidence
Finding

L192 明确说明实时流式转写需升级到付费版,表示当前基础版不支持该能力。可是在 L271 又将“将转录结果用于语音助手,实现语音到文字的实时转换”列为本技能应用场景,主动传达了相反能力边界。

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Low
Category
Not specified by scanner
Confidence
81% confidence
Finding

L278-L280 使用“已实现以下异常处理与可靠性保障”这一表述,宣称具体可靠性机制已经实现。该文件其余内容仅包含用法说明和示例,没有任何实际实现这些检查或降级逻辑的代码/步骤,构成文档对实际内容的误述。

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.