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Security audit

Azure Ai Voicelive Py

Security checks for vulnerabilities and agentic risk

Overview

This is a documentation-only Azure voice app helper whose network, microphone, and credential examples match its stated purpose, with privacy and dependency-hardening notes users should consider.

Before installing or building from these examples, use a virtual environment, consider pinning dependencies, store Azure keys securely, and make sure apps clearly tell users when microphone or audio-file content is sent to Azure for processing and when generated audio is saved locally.

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:14
Finding

Unpinned Third-Party Dependencies Allow Unreviewed Supply-Chain Changes

Content
View full analysis

Vulnerability Details

File Location: SKILL.md:14
Vulnerability Type: Unpinned third-party dependency installation
Risk Level: Medium

bash
pip install azure-ai-voicelive aiohttp azure-identity

Technical Analysis

The installation command does not constrain dependency versions or verify package integrity with hashes. Consequently, executing it resolves whatever compatible package versions are available from the configured Python package index at installation time.

The listed package names are consistent with the Skill's declared Azure Voice Live functionality, and no evidence of typosquatting or an intentionally malicious package was found. Nevertheless, the absence of reviewed version pins and integrity verification makes installations non-reproducible and permits unreviewed upstream releases or compromised transitive dependencies to enter the environment.

Attack Path

  1. An upstream package account, release pipeline, package-index entry, or transitive dependency is compromised.
  2. The attacker publishes a malicious release that satisfies the unconstrained installation command.
  3. A user follows the Skill's installation instructions.
  4. pip resolves and installs the malicious or compromised release.
  5. Malicious package code runs during installation, import, or application execution with the privileges of the invoking user.

This path depends on an upstream supply-chain compromise; the audited project does not itself retrieve or execute a hidden payload.

Impact Assessment

A compromised dependency could execute code with the installing user's privileges. Depending on the runtime environment, this could expose local files, environment variables, Azure credentials available through the credential chain, microphone data, and network access. It could also alter application behavior or transmit data to unauthorized destinations.

The affected scope is the Python environment and user accou ...[truncated 137 chars]

Remediation
View remediation

Remediation Suggestions

  1. Pin each direct dependency to a reviewed version.
  2. Generate a lock file that also constrains transitive dependencies.
  3. Record and enforce package hashes, such as with:
    bash
    pip install --require-hashes -r requirements.txt
    
  4. Obtain packages only from a trusted, explicitly configured package index.
  5. Use an isolated virtual environment with no unnecessary credentials or permissions.
  6. Add automated dependency vulnerability and provenance checks to the release process.
  7. Periodically update pins through a controlled review and testing workflow rather than resolving unrestricted versions during installation.
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
Findings (5)

Ae1

High
Category
analysis-evasion
Confidence
100% confidence
Finding

Referenced artifact was not completely inspected

Content

Scanner excerpt · SKILL.md (reported line 309)May include surrounding context.

md
- **All Models & Types**: See [references/models.md](references/models.md)

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
92% confidence
Finding

The skill demonstrates microphone audio capture and real-time streaming to a remote Azure AI service but does not mention obtaining user consent, providing a recording/transmission disclosure, or handling sensitive voice data appropriately. In a voice application skill, omission of privacy guidance can lead developers to build experiences that capture and transmit user speech without adequate notice, increasing legal, privacy, and trust risks.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
88% confidence
Finding

This markdown file documents connecting to a remote service using authentication credentials and sending model inputs, including text and audio, but it does not include any warning that user content and credentials-related metadata are transmitted to an external service. Because SQP-2 applies to markdown files when descriptions omit warnings about behaviors affecting user data or privacy, this omission is a relevant documentation safety gap.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
92% confidence
Finding

The examples stream live microphone audio to a remote Azure AI endpoint but do not include any explicit notice that spoken content will be transmitted off-device for processing. In a voice SDK skill, this omission can lead developers to build UX flows that collect sensitive speech without clear consent, creating privacy, compliance, and user-trust risks.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The audio file processing example reads an audio file, uploads its contents to a cloud AI service, and writes returned audio to disk without warning about either data transfer or local persistence. That can cause developers to unintentionally process sensitive recordings or store generated audio artifacts in insecure locations, increasing privacy and data-handling exposure.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.