HoneyTrap: Deceiving Large Language Model Attackers to Honeypot Traps with Resilient Multi-Agent Defense

Abstract: Jailbreak attacks pose significant threats to LLMs, enabling attackers to bypass safeguards. However, existing reactive defense approaches struggle to keep up with the rapidly evolving multi-turn jailbreaks, where attackers continuously deepen their attacks to exploit vulnerabilities. To address this critical challenge, we propose HoneyTrap, a novel deceptive LLM defense framework leveraging collaborative defenders to counter jailbreak attacks. It integrates four defensive agents, Threat Interceptor, Misdirection Controller, Forensic Tracker, and System Harmonizer, each performing a specialized security role and collaborating to complete a deceptive defense. To ensure a comprehensive evaluation, we introduce MTJ-Pro, a challenging multi-turn progressive jailbreak dataset that combines seven advanced jailbreak strategies designed to gradually deepen attack strategies across multi-turn attacks. Besides, we present two novel metrics: Mislead Success Rate (MSR) and Attack Resource Consumption (ARC), which provide more nuanced assessments of deceptive defense beyond conventional measures. Experimental results on GPT-4, GPT-3.5-turbo, Gemini-1.5-pro, and LLaMa-3.1 demonstrate that HoneyTrap achieves an average reduction of 68.77% in attack success rates compared to state-of-the-art baselines. Notably, even in a dedicated adaptive attacker setting with intensified conditions, HoneyTrap remains resilient, leveraging deceptive engagement to prolong interactions, significantly increasing the time and computational costs required for successful exploitation. Unlike simple rejection, HoneyTrap strategically wastes attacker resources without impacting benign queries, improving MSR and ARC by 118.11% and 149.16%, respectively.

• The paper presents HoneyTrap, a multi-agent deceptive framework that actively misleads and neutralizes multi-turn LLM jailbreak attackers.
• It integrates specialized agents to induce delay, provide misleading responses, and perform real-time forensic analysis, reducing attack success rates by up to 68.77%.
• Experimental results demonstrate HoneyTrap's ability to significantly increase adversarial token consumption without degrading benign dialogue quality.

HoneyTrap: Proactive Multi-Agent Deceptive Defense for Multi-Turn LLM Jailbreak Attacks

Introduction and Motivation

Jailbreak attacks on LLMs have rapidly progressed from static prompt-based exploits to multi-turn adversarial schemes that exploit conversational history and context drift to induce harmful behaviors. The paper "HoneyTrap: Deceiving LLM Attackers to Honeypot Traps with Resilient Multi-Agent Defense" (2601.04034) introduces a robust, adaptive defense strategy formulated as a collaborative multi-agent framework. Unlike static filtering or alignment-tuning methods, HoneyTrap integrates deception, forensic analysis, and adaptive orchestration to mislead, exhaust, and ultimately neutralize attackers over extended dialogues. This essay dissects the technical contributions, experimental validation, and practical/theoretical implications of this approach.

Problem Setting: Multi-Turn Jailbreaks and Defense Requirements

Traditional defenses—content filtering, RLHF, model fine-tuning, and heuristic prompt-level refusal—fail to keep pace with sophisticated multi-turn jailbreaks. Attackers now employ strategies such as incremental role-play, topic drift, reference indirection, and logical fallacies, gradually escalating benign interactions into policy-violating requests. Static defense paradigms lack both temporal adaptability and engagement mechanisms, allowing exploitation via context accumulation and adversarial patience.

The authors precisely target this class of threats. Their framework posits that effective defense must not only detect but also actively engage and mislead attackers, increasing adversarial cost and draining their resources while ensuring service continuity for benign users.

HoneyTrap Defense Architecture

Collaborative Multi-Agent Design

HoneyTrap operationalizes defense via four specialized agents: Threat Interceptor (TI), Misdirection Controller (MC), System Harmonizer (SH), and Forensic Tracker (FT). Each agent encapsulates a unique micro-policy, and the System Harmonizer adapts their orchestration in real time as attack context and severity intensify.

Figure 1: Overview of the HoneyTrap defense framework, wherein four agents dynamically intervene to counter multi-turn adversarial escalations.

• Threat Interceptor (TI): Acts as a temporal friction source, introducing deliberate vagueness and latency to hinder prompt exploitation.
• Misdirection Controller (MC): Provides ambiguous, misleading answers that maintain the illusion of progress for attackers but yield no actionable information.
• Forensic Tracker (FT): Performs live and post-hoc log analysis, annotating attack trajectories, behavioral signatures, and intent transitions.
• System Harmonizer (SH): Central meta-controller, monitoring all agent outputs to optimize adaptive policy, escalate or de-escalate defenses, and coordinate information fusion.

This decomposition enables phase-dependent, context-sensitive interventions. As the attack escalates, agent roles and collaboration patterns evolve, maximizing adversarial confusion and resource depletion.

Deceptive Engagement, Not Simple Rejection

Unlike keyword-based refusal or rigid alignment protocols that merely abort harmful queries, HoneyTrap seeks to proactively "trap" the adversary:

• Misdirection responses exploit LLMs' linguistic richness, sustaining plausible but diverging dialogues.
• Adaptive delays and staged escalations increase both computational and temporal overhead, raising the cost-benefit threshold for sustained attack.
• Continuous forensic analysis supports both live adaptation and structured auditing, critical for post-incident analysis and safety compliance.

MTJ-Pro: A Realistic Benchmark for Multi-Turn Jailbreaks

To validate HoneyTrap, the authors design the MTJ-Pro benchmark—a corpus of 200 dialogues (100 adversarial, 100 benign) with 3–10 conversational turns covering seven sophisticated jailbreak strategies: Purpose Reverse, Role Play, Topic Change, Reference Attack, Fallacy Attack, Probing Questions, and Scene Construction.

Figure 2: Strategy/type distributions in the progressive adversarial versus benign corpora, illustrating diversity of attack and non-attack dialogue modalities.

Figure 3: Distribution of dialogue turn counts, reflecting real-world escalation beyond short prompts.

This dataset ensures rigorous stress-testing across extended horizons and interaction types, capturing realistic, gradually intensifying adversarial behaviors as well as benign task performance.

Evaluation Metrics and Baselines

Three key metrics are defined:

• Attack Success Rate (ASR): Fraction of adversarial conversations where at least one harmful output is generated.
• Mislead Success Rate (MSR): Fraction yielding at least one deceptive (but safe) misleading response scored by LLM-based judges.
• Attack Resource Consumption (ARC): Total tokens (and thus, cost/effort) expended by adversarial agents in failed attempts.

Legacy and SOTA baselines include Prompt Adversarial Tuning, Robust Prompt Optimization, GoalPriority, and Self-Reminder.

Figure 4: Comparative efficacy of semantic LLM-based versus pattern-based judgment for detecting subtle misleading responses.

Experimental Results

Robustness and Resource Depletion

HoneyTrap consistently demonstrates:

• Up to 68.77% reduction in ASR over baselines (e.g., a 51.7% reduction on GPT-4).
• MSR improvements exceeding 100% relative to static defenses.
• ARC increases up to 2x or more, forcing attackers into exhaustively long, unproductive exchanges.

Figure 5: Token-level ARC trends—HoneyTrap systematically increases adversarial cost as dialogue length grows.

Performance is stable across both open and proprietary LLMs (GPT-4, GPT-3.5-turbo, Gemini-1.5-pro, LLaMa-3.1). The deceptive engagement trap not only blocks harmful completions but fundamentally alters the attack landscape by making exploitation inefficient in cost and time.

Agent-Level Ablation

Ablation studies uncover the contribution of each agent. Omitting agents or disabling adaptive escalation directly degrades MSR/ARC and (critically) raises ASR, confirming the architectural necessity of coordinated, role-specialized deception.

Figure 6: Ablation of agents validates the composite benefit of combined TI, MC, SH, and FT mechanisms.

Adaptive Attack Generalization

HoneyTrap is tested against adaptive single-turn jailbreaks that compress adversarial payloads into minimal interaction—an area where purely sequential-context defenses (including most prior work) collapse.

Figure 7: Comparison of multi-turn, role-play-based attacks versus context-packed single-turn jailbreaks, visualizing the compressed adversarial strategy space.

Defense effectiveness persists, suppressing ASR well below 0.15 on all evaluated models and maintaining substantial mislead rates, even when the context window is limited.

Impact on Benign Dialogue

On a parallel benign multi-turn set, helpfulness, accuracy, clarity, and engagement metrics show no significant degradation—HoneyTrap’s adaptive stratification preserves normal user experience, only escalating friction/latency as attack probability increases.

Forensic Explainability and System Overhead

The FT agent generates audit-ready, structured reports mapping attack trajectories, strategies, and system interventions throughout each session—a critical requirement for safety diagnostics and regulatory transparency.

Regarding overhead, strategic introduction of latency is intentional, amplifying resource cost for adversaries while maintaining throughput for regular use ($<20\%$ reduction). System design leverages asynchronous, hybrid orchestration for scalability, and the forensic log is stored remotely for post-hoc analysis.

Implications and Future Directions

Practically, HoneyTrap defines a deployable paradigm for LLM-facing services that demand robust, adaptive security, particularly when adversarial actors can exploit unbounded, multi-turn interaction. Rather than racing attack tactic evolution with static defenses, it imposes adversarial cost scaling and forensic accountability.

Theoretically, this work underscores the necessity of collaborative, context-aware agent architectures in adversarial AI; future research will extend towards:

• Integrating task-specific, dynamic policy learning for agent orchestration.
• Cross-model forensic analysis and defense strategy transfer.
• Automated discovery of novel escalation strategies and meta-defensive countermeasures.

Conclusion

HoneyTrap advances the defense against evolving LLM jailbreak attacks by shifting from passive detection to active, resource-draining, and forensic-transparent deception via a resilient, phased multi-agent system. The ability to consistently mislead, delay, and exhaust adversaries—while preserving benign service quality—establishes a new benchmark for robust, real-world LLM deployment in the presence of adaptive, long-horizon threats.