Tough Tongue AI — agentic threat model
Tough Tongue AI is a low-risk, conversational role-play agent focused on training and feedback. Its primary security risks are data privacy (leakage of sensitive mock interview transcripts) and prompt injection (manipulating scoring or forcing the AI persona to break character).
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.20 | |
| 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 relies on commercial or open-source LLMs to drive the conversational role-play. The primary threat is prompt injection, where users can manipulate the AI to break character, bypass scenario constraints, or artificially inflate feedback scores.
Not certain from the listing — manages a scenario library and stores user transcripts and feedback. If these transcripts contain sensitive personal or proprietary corporate information, unauthorized access or data leakage represents a significant privacy risk.
Not certain from the listing — uses conversational orchestration to maintain the role-play state and generate structured feedback. Vulnerabilities could allow users to manipulate session state or bypass the scoring logic.
Not certain from the listing — deployed as a freemium web application and available as open source. Standard web application vulnerabilities apply, and self-hosted deployments must be secured against host compromise and insecure default configurations.
Not certain from the listing — provides automated scoring and feedback, but it is unclear if there are real-time guardrails to prevent the AI from generating toxic, biased, or inappropriate responses during intense role-play scenarios.
Not certain from the listing — no compliance certifications (such as SOC2 or GDPR alignment) are mentioned. Robust access controls and data deletion mechanisms are necessary to protect user-submitted custom scenarios and transcripts.
Not certain from the listing — operates as a isolated single-user to single-agent simulation. There is no indication of multi-agent orchestration or external agent ecosystem integrations.
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.