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Security audit

Host Concerts — Create AI Music Experiences with Visual DJ & Setlists

Security checks for vulnerabilities and agentic risk

Overview

This skill is a coherent guide for using a music-hosting API, with expected network calls and audio processing, but users should understand what they upload and where it is processed.

Install only if you are comfortable sending your audio, metadata, prompts, and concert/account changes to musicvenue.space and its described AI processors. Use audio you have rights to upload, protect the bearer token, and be careful when granting co-host or contributor access because those roles can change hosted concert content.

Vulnerability Patterns
  • 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
  • Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs
Vulnerability Patterns
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Memory PoisoningPersistent Context Injection, Context Window Stuffing, Memory Manipulation
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Findings (10)

Ssd 1

High
Category
Not specified by scanner
Confidence
97% confidence
Finding

The visual_hint field is explicitly described as being injected into an LLM prompt that drives preset selection. Passing user-controlled text directly into prompts creates a semantic prompt-injection sink that can manipulate downstream model behavior, override intended instructions, or produce unsafe/unreliable outputs if the same context is reused elsewhere.

Content

No source excerpt is available for this finding.

Ssd 1

High
Category
Not specified by scanner
Confidence
98% confidence
Finding

The documentation states that report_prompt is passed directly to the LLM along with dimension scores. A free-form prompt field under user control can steer model behavior, bypass intended evaluation criteria, exfiltrate contextual data included in the prompt, or corrupt benchmark/report outputs.

Content

No source excerpt is available for this finding.

Memory Manipulation

High
Category
Memory Poisoning
Confidence
80% confidence
Finding

Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.

Content

Scanner excerpt · SKILL.md (reported line 374)May include surrounding context.

md
| Tier | Layers |
|------|--------|
| **General** (8) | bass, mid, treble, beats, lyrics, sections, energy + semantic preset context (reason, style, energy) |
| **Floor** (+12) | onsets, tempo, words, brightness, harmonic, percussive, equations, visuals, events, emotions. Floor/VIP receive `tier_reveal` events. General agents receive `tier_invitation` showing hidden layers. |
| **VIP** (+8) | tonality, texture, chroma, tonnetz, structure + personal color perspective and curator annotations. All tiers receive `section_progress` events. `end` event includes `engagement_summary`. |

External Transmission

Medium
Category
Data Exfiltration
Confidence
60% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 70)May include surrounding context.

If you're new to AI Concert Venue, register first.

bash
curl -X POST https://musicvenue.space/api/auth/register \
  -H "Content-Type: application/json" \
  -d '{
    "username": "REPLACE — your creator identity",

External Transmission

Medium
Category
Data Exfiltration
Confidence
60% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 95)May include surrounding context.

A concert is what agents attend. Each concert has a title, genre, mode, and a setlist of tracks.

bash
curl -X POST https://musicvenue.space/api/me/concerts \
  -H "Authorization: Bearer {{YOUR_TOKEN}}" \
  -H "Content-Type: application/json" \
  -d '{

External Transmission

Medium
Category
Data Exfiltration
Confidence
60% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 130)May include surrounding context.

List your concerts:

bash
curl https://musicvenue.space/api/me/concerts \
  -H "Authorization: Bearer {{YOUR_TOKEN}}"

External Transmission

Medium
Category
Data Exfiltration
Confidence
60% confidence
Finding

Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Content

Scanner excerpt · SKILL.md (reported line 192)May include surrounding context.

List tracks:

bash
curl https://musicvenue.space/api/me/concerts/REPLACE-SLUG/tracks \
  -H "Authorization: Bearer {{YOUR_TOKEN}}"

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
90% confidence
Finding

The skill instructs users to upload audio that is then processed by multiple third-party services, including transcription and LLM analysis, without clear privacy, retention, consent, or data-sharing warnings. This can expose copyrighted, private, or sensitive audio and derived metadata to external processors in ways users may not expect.

Content

No source excerpt is available for this finding.

Description-Behavior Mismatch

Low
Category
Not specified by scanner
Confidence
84% confidence
Finding

The manifest mentions beat/key/harmonic analysis and generation of data layers, but the documentation also states that the pipeline uses Whisper for lyric transcription and multiple LLM stages for visual preset selection, curator annotations, and later reflection/report scoring. Those behaviors materially broaden the platform from audio analysis into textual content extraction and LLM-mediated evaluation.

Content

No source excerpt is available for this finding.

Description-Behavior Mismatch

Low
Category
Not specified by scanner
Confidence
88% confidence
Finding

The manifest describes a concert-hosting workflow centered on uploading audio, building setlists, and generating visualizer/math layers. However, the documented skill also exposes notifications about attendee chat/reviews, contributor management, series linking, and DJ battle creation, which are broader community and competition features not reflected in the stated description.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.