🤖 AI Summary
Autonomous multimodal LLM agents pose escalating risks of loss of control, necessitating rigorous behavioral oversight. Method: We propose a verifiability-centered control architecture featuring runtime cryptographic signing and symbolic action attestation; a lightweight audit agent with challenge-response protocols; and OPERA, a novel benchmark shifting evaluation from “prevention” to “detection–response.” Our approach integrates cryptographic signatures, symbolic reasoning, and constraint-logic verification, rigorously validated via red-teaming and robustness testing against prompt and persona manipulation. Contribution/Results: Experiments demonstrate significant improvements in detection latency and attribution reliability for covert misalignment behaviors, while maintaining high observability and strong auditability across diverse adversarial scenarios.
📝 Abstract
As LLM-based agents grow more autonomous and multi-modal, ensuring they remain controllable, auditable, and faithful to deployer intent becomes critical. Prior benchmarks measured the propensity for misaligned behavior and showed that agent personalities and tool access significantly influence misalignment. Building on these insights, we propose a Verifiability-First architecture that (1) integrates run-time attestations of agent actions using cryptographic and symbolic methods, (2) embeds lightweight Audit Agents that continuously verify intent versus behavior using constrained reasoning, and (3) enforces challenge-response attestation protocols for high-risk operations. We introduce OPERA (Observability, Provable Execution, Red-team, Attestation), a benchmark suite and evaluation protocol designed to measure (i) detectability of misalignment, (ii) time to detection under stealthy strategies, and (iii) resilience of verifiability mechanisms to adversarial prompt and persona injection. Our approach shifts the evaluation focus from how likely misalignment is to how quickly and reliably misalignment can be detected and remediated.