math-olympiad — agentic threat model
This agent presents a low overall risk posture due to its specialized focus on mathematical proof verification and lack of real-world write access, though its multi-agent adversarial verification loop introduces non-deterministic execution paths.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.80 | |
| 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.
Utilizes Anthropic foundation models optimized for mathematical reasoning. Vulnerable to adversarial proof inputs designed to bypass the verifier or trigger false positives/negatives in proof validation.
Not certain from the listing — likely relies on static mathematical datasets (IMO, Putnam, USAMO) and fresh-context execution, but data ingestion pipelines for new math problems could be vulnerable to prompt injection or data poisoning.
Orchestrates subagents and skills to generate and adversarially check proofs. Vulnerabilities include logic flaws in the adversarial feedback loop that could cause infinite loops or state exhaustion during complex proof verification.
Not certain from the listing — requires a secure execution environment (sandboxing) to run code-based proof verifiers or mathematical solvers safely, preventing arbitrary code execution from untrusted proof inputs.
Features calibrated abstention over bluffing to mitigate hallucination risks. However, blind spots may exist if the adversarial verifiers fail to detect novel, mathematically unsound proof patterns.
Not certain from the listing — lacks explicit mention of access controls, licensing compliance for training data, or audit logging of the proof generation and verification steps.
Employs a multi-agent architecture consisting of generator and verifier subagents. Vulnerable to cascading failures if the adversarial verifier subagent is compromised or systematically bypassed by the generator.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).