Listen Labs — agentic threat model
Listen Labs presents a moderate risk profile primarily centered on data privacy and conversational integrity, as it dynamically conducts customer interviews and processes sensitive qualitative feedback without explicit security controls detailed in its public listing.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.50 |
Scored with the canonical OWASP AIVSS formula (AIVSS calculator reference); agentic risk factors estimated from the agent’s described capabilities.
MAESTRO 7-layer threat model
Per-layer threats for this agent. Layers tagged “not certain from listing” are general, caveated commentary where the public description didn’t pin that layer.
Not certain from the listing — likely utilizes commercial LLMs to dynamically generate interview questions and analyze responses. Primary threats include prompt injection by interviewees to hijack the conversational flow or extract system prompts, and misaligned outputs that could damage brand reputation.
Not certain from the listing — ingests and stores customer interview transcripts, audio/video recordings, and synthesized insights. Key threats include data exfiltration of sensitive customer PII and proprietary feedback, as well as data poisoning if malicious users feed deceptive information during interviews.
Not certain from the listing — orchestrates conversational state and analysis pipelines. Threats include insecure integration with transcription or synthesis tools, and session-state manipulation where interviewees attempt to corrupt the agent's memory of the current session.
Not certain from the listing — presumably hosted as a web-based SaaS platform. Standard web application threats apply, including unauthorized access to cloud databases containing customer research and potential API vulnerabilities in the interview delivery interface.
Not certain from the listing — requires robust guardrails to ensure the AI interviewer remains professional, unbiased, and on-topic. A lack of real-time monitoring could allow inappropriate conversational drift or offensive outputs to go undetected during live customer interactions.
Not certain from the listing — handling customer voice/text data necessitates strict compliance with GDPR, CCPA, and consent management frameworks. The listing does not specify encryption standards, access controls, or data retention policies.
Not certain from the listing — operates primarily as a standalone research platform, though it may integrate with external CRMs or product management tools. Risks are limited to unauthorized data sharing via these integrations rather than complex multi-agent cascading failures.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.