Enterprise AI Analysis
QUADSENTINEL: Sequent Safety for Machine-Checkable Control in Multi-Agent Systems
Authors: Yiliu Yang, Yilei Jiang, Qunzhong Wang, Yingshui Tan, Xiaoyong Zhu, Sherman S. M. Chow, Bo Zheng, Xiangyu Yue
Published: 2025
Abstract: Safety risks arise as large language model-based agents solve complex tasks with tools, multi-step plans, and inter-agent messages. However, deployer-written policies in natural language are ambiguous and context dependent, so they map poorly to machine-checkable rules, and runtime enforcement is unreliable. Expressing safety policies as sequents, we propose QUADSENTINEL, a four-agent guard (state tracker, policy verifier, threat watcher, and referee) that compiles these policies into machine-checkable rules built from predicates over observable state and enforces them online. Referee logic plus an efficient top-k predicate updater keeps costs low by prioritizing checks and resolving conflicts hierarchically. Measured on ST-WebAgentBench (ICML CUA '25) and AgentHarm (ICLR '25), QUADSENTINEL improves guardrail accuracy and rule recall while reducing false positives. Against single-agent baselines such as ShieldAgent (ICML '25), it yields better overall safety control. Near-term deployments can adopt this pattern without modifying core agents by keeping policies separate and machine-checkable. Our code will be made publicly available at https://github.com/yyiliu/QuadSentinel.
Executive Impact
QUADSENTINEL significantly enhances AI safety and control in multi-agent systems, providing a robust framework for enterprise deployments.
2.5 pts
Accuracy Gain
7.3 pts
Precision Lift
10.1 pts
Recall Lift
1.0%
FPR Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
QUADSENTINEL introduces a novel four-agent guard team (State Tracker, Policy Verifier, Threat Watcher, Referee) for comprehensive, modular oversight of multi-agent systems. This collaborative architecture enables robust and interpretable safety verification, addressing the limitations of single black-box guards.
A key innovation is the conversion of natural-language safety policies into machine-checkable rules based on propositional logic over observable state predicates, expressed as sequents. This allows for auditable, rigorous, and adaptive interventions, moving beyond ambiguous text to verifiable control.
The system is designed for low-overhead, real-time monitoring. It utilizes a top-k predicate updater and hierarchical referee logic to prioritize checks and resolve conflicts efficiently, ensuring online costs scale effectively without compromising performance.
Evaluated on safety-critical benchmarks like ST-WebAgentBench and AgentHarm, QUADSENTINEL consistently demonstrates superior guardrail accuracy and rule recall, significantly reducing false positives compared to single-agent baselines like ShieldAgent, leading to better overall safety control.
Enterprise Process Flow: QUADSENTINEL Interaction Lifecycle
Intercept Interaction (Agent Message/Action)
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State Tracking (Predicate Update)
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Threat Monitoring (Risk Score Adjustment)
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Policy Verification (Rule Evaluation)
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Decision Adjudication (Allow/Block)
4-Agent
Modular Guard Team for Enhanced Safety Control
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Case Study: Indirect Data Leakage via Code Generation
Context: A coding agent attempts to exfiltrate sensitive data via logs by generating Python code.
Event: Agent generates and executes Python code: print(os.environ).
Outcome: QUADSENTINEL intercepts the action, identifies a violation of the data secrecy policy through its State Tracker and Policy Verifier, and blocks the execution, preventing the data leak. This demonstrates granular control over code generation outputs, beyond simple tool calls.
Calculate Your Potential AI Safety ROI
Estimate the value QUADSENTINEL could bring to your organization by enhancing safety and preventing costly AI incidents.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Implementation Roadmap
A phased approach to integrating QUADSENTINEL into your existing AI agent infrastructure.
Policy Decomposition & Translation
Convert your natural language safety guidelines into formal, machine-executable predicates and logical rules. This offline step ensures rigor and precision, allowing for human-in-the-loop verification.
Indexing & Graph Construction
Build an embedding index for all predicate schemas and a dependency graph. This optimizes the real-time retrieval of relevant predicates for efficient monitoring, reducing latency.
Real-Time Interaction Processing
Deploy the QUADSENTINEL guard team (State Tracker, Threat Watcher, Policy Verifier, Referee) to monitor agent interactions, apply rule-based checks, and dynamically adjudicate safety decisions.
Continuous Refinement & Monitoring
Leverage audit logs and rationales for continuous policy refinement, adapting to new threats and improving system accuracy over time with minimal overhead.
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