AI Haggler — agentic threat model
AI Haggler presents a moderate-to-high risk profile due to its active real-world interaction via outbound telephony, making it a potential vector for automated vishing, toll fraud, and regulatory non-compliance if compromised.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.80 | |
| Opacity & Reflexivity | 0.60 |
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 a commercial LLM combined with speech-to-text and text-to-speech models. Primary threats include voice-based prompt injection (e.g., hotel staff tricking the agent into agreeing to unfavorable terms or leaking system prompts) and model misalignment leading to inappropriate verbal behavior during calls.
Not certain from the listing — likely stores user-submitted hotel phone numbers, negotiation parameters, and call transcripts. Threats include unauthorized access to call logs containing sensitive user or hotel representative data, and potential data exfiltration of proprietary negotiation strategies.
Not certain from the listing — likely uses a custom orchestration loop to manage the negotiation state machine and trigger telephony APIs. Threats include tool misuse, where an attacker manipulates the agent into calling premium-rate numbers or executing unauthorized outbound calls.
Not certain from the listing — hosted as a web application integrated with third-party telephony APIs (e.g., Twilio, Bland AI). Threats include exposure of telephony API keys, lack of sandboxing for the call-generation environment, and infrastructure compromise leading to massive toll fraud.
Not certain from the listing — features 'call reporting' for users to review results. Threats include insufficient logging of adversarial inputs (e.g., voice prompt injections) and a lack of real-time guardrails to detect and terminate calls if the agent is hijacked or begins hallucinating.
Not certain from the listing — involves user registration and a credit system. Major threats include non-compliance with local call-recording consent laws (two-party consent states) and TCPA regulations, alongside potential abuse of the credit system to conduct spam campaigns.
Not certain from the listing — currently acts as a single agent interacting with human hotel staff. Future threats include cascading negotiation loops or mutual exploitation if the agent interacts directly with hotel-side AI receptionists or booking agents.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).