Physics gpt — agentic threat model
Physics GPT is a low-risk educational and analytical agent with minimal autonomy, primarily vulnerable to input-based exploits like LaTeX injection, malicious image uploads, and prompt injection rather than systemic agentic failures.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.30 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.40 | |
| Opacity & Reflexivity | 0.20 |
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.
Utilizes a specialized ChatGPT model optimized for physics. Primary threats include adversarial prompt injection to bypass educational guardrails, model reprogramming, and generating plausible-sounding but incorrect physics explanations (hallucinations).
Not certain from the listing — No details are provided regarding the training data pipeline, RAG sources, or vector databases. Potential risks include data poisoning of the specialized physics knowledge base and data exfiltration via user-uploaded images or equations.
Not certain from the listing — The orchestration framework is unspecified. The presence of a 'Circuit Analyzer' and 'LaTeX Math Rendering' suggests specialized tool integrations that could be vulnerable to input manipulation, such as LaTeX injection attacks or malicious circuit diagram parsing.
Not certain from the listing — No hosting, sandboxing, or infrastructure details are provided. Risks include server-side exploitation through malicious image uploads or remote code execution via vulnerable LaTeX rendering libraries.
Not certain from the listing — No logging, monitoring, or guardrail mechanisms are described. Gaps in observability could allow users to abuse the API or systematically bypass safety filters without detection.
Not certain from the listing — No identity management, access control policies, or compliance standards (such as GDPR or SOC2) are mentioned, which is critical given the API access and freemium model.
The agent operates as a standalone vertical tool with no described multi-agent coordination or marketplace ecosystem, resulting in minimal risk of cascading multi-agent failures.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).