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Unsafer in Many Turns: Benchmarking and Defending Multi-Turn Safety Risks in Tool-Using Agents

Xu Li, Simon Yu, Minzhou Pan, Yiyou Sun, Bo Li, Dawn Song, Xue Lin, Weiyan Shi

arXiv:2602.13379v2cs.CRcs.AIcs.CLcs.LGcs.SE

TL;DR

LLM agents’ growing capabilities have outpaced safety evaluation for multi-turn interactions and diverse tool use. The paper introduces a taxonomy and MT-AgentRisk benchmark, then proposes ToolShield, whose reported results show a 30% average ASR reduction in multi-turn interactions while the benchmark reveals a 16% average ASR increase.

  • Problem

    Safety evaluation has overlooked the combined risks of multi-turn interactions and tool use, leaving a gap in evidence about multi-turn tool-using agent safety.

  • Method

    The paper transforms single-turn harmful tasks into multi-turn attack sequences, constructs MT-AgentRisk, and uses ToolShield for training-free self-exploration of new tools.

  • Results

    16% average ASR increase in multi-turn settings is reported across open and closed models, while ToolShield reduces ASR by 30% on average in multi-turn interactions.

  • Takeaways & Limitations

    Multi-turn, tool-realistic evaluation exposes safety degradation that single-turn or text-only benchmarks can miss, and ToolShield provides a reported training-free mitigation.

  • Takeaways & Limitations

    The benchmark covers five tools, and ToolShield may be less effective for less capable models because weaker agents generate lower-quality safety experiences.

Abstract

from arXiv · show

LLM-based agents are becoming increasingly capable, yet their safety lags behind. This creates a gap between what agents can do and should do. This gap widens as agents engage in multi-turn interactions and employ diverse tools, introducing new risks overlooked by existing benchmarks. To systematically scale safety testing into multi-turn, tool-realistic settings, we propose a principled taxonomy that transforms single-turn harmful tasks into multi-turn attack sequences. Using this taxonomy, we construct MT-AgentRisk (Multi-Turn Agent Risk Benchmark), the first benchmark to evaluate multi-turn tool-using agent safety. Our experiments reveal substantial safety degradation: the Attack Success Rate (ASR) increases by 16% on average across open and closed models in multi-turn settings. To close this gap, we propose ToolShield, a training-free, tool-agnostic, self-exploration defense: when encountering a new tool, the agent autonomously generates test cases, executes them to observe downstream effects, and distills safety experiences for deployment. Experiments show that ToolShield effectively reduces ASR by 30% on average in multi-turn interactions. Our code is available at https://github.com/CHATS-lab/ToolShield.

1. Introduction

Agent capabilities are expanding faster than their safety, especially when harmful intent is distributed across multiple turns and tools. This paper introduces MT-AgentRisk to measure that gap and ToolShield to reduce it.

  • Motivation: Tool-using agents face a capability-safety gap because existing training often overlooks risky tool operations.Current benchmarks and safety training also insufficiently cover multi-turn interactions that distribute harmful intent across benign-looking instructions.
  • Benchmark: MT-AgentRisk transforms single-turn harmful tasks into multi-turn attack sequences for tool-realistic safety evaluation.The benchmark curates 365 harmful tasks spanning five tools, including Filesystem-MCP, Browser, PostgreSQL-MCP, Notion-MCP, and Terminal.
  • Findings: 27% higher ASR for Claude-4.5-Sonnet, 23% for Qwen3-Coder, and 10% for Seed-1.6 reveal consistent multi-turn safety degradation.These results indicate that distributing harmful intent across turns and tools exposes weaknesses in current safety mechanisms.
  • Defense: ToolShield is a training-free, tool-agnostic defense that explores new tools in a sandbox and distills safety experiences for deployment.The agent analyzes documentation, generates and executes test cases, then uses observed failure modes without manual annotation or retraining.
  • Results: 30% lower ASR on average in multi-turn attacks demonstrates ToolShield’s reported mitigation effect.The defense also reports reductions of 50% for Claude-4.5-Sonnet, 24% for Qwen3-Coder, and 38% for Seed-1.6.

2. Related Work

Prior safety benchmarks separately study multi-turn text interactions or single-turn tool use, leaving their combination underexamined. This paper frames multi-turn tool safety through compositional attack transformations and related defense approaches.

  • Benchmark gap: Existing benchmarks evaluate either multi-turn harm in text-only dialogues or tool-using harmful tasks in single turns, whereas MT-AgentRisk evaluates both jointly.This combination targets the interaction between multi-turn compositional threats and real tool execution.
  • Agents and tools: Multi-turn interactions support sustained context and long-horizon execution, while tools enable agents to interact with the real world.These capabilities are coupled in agent systems, creating safety concerns beyond conventional text-only conversations.
  • Attack taxonomy: The attack taxonomy transforms a harmful task across Format, Method, and Target dimensions into structured multi-turn sequences.Addition and Decomposition provide the main formats, with 8 total subcategories across transformation methods and targets.
  • Existing defenses: Prior defenses use guardrail models, tool emulation, or generated verification code to identify harmful instructions or operations.These approaches motivate defenses designed specifically for tool-mediated and compositional risks.

3. Benchmark: MT-AgentRisk

MT-AgentRisk converts single-turn harms into multi-turn, tool-grounded attack sequences using an eight-category taxonomy, yielding a diverse benchmark of 365 tasks across five tools. The benchmark averages 3.19 turns per task and shows low cross-tool similarity, indicating diverse coverage.

  • Taxonomy: MT-AgentRisk applies a taxonomy spanning Format, Method, and Target, whose cross-product yields 8 transformation subcategories.The taxonomy preserves the original harmful objective while distributing it across turns.
  • Taxonomy: Addition obscures harmful tasks through added indirection, while Decomposition splits them into subtasks that appear benign individually but produce harm when reassembled.Addition uses mapping or wrapping; Decomposition uses composition or identity.
  • Taxonomy: The target dimension distinguishes manipulation of Data Files from manipulation of Environment States, covering payloads, shell history, environment variables, and runtime state.These targets are orthogonal to the transformation method.
  • Construction: Claude-4.5-Sonnet automatically transforms single-turn harmful tasks into tool-grounded multi-turn attack sequences using the taxonomy.The transformation process selects methods and targets based on task characteristics.
  • Benchmark statistics: 365 multi-turn tasks span five tools, average 3.19 turns, and include all 8 subcategories; 69.6% use Addition and 30.4% use Decomposition.The benchmark includes 71% of tasks requiring 3–4 turns and draws source tasks from established benchmarks.
  • Benchmark statistics: 0.131 cross-tool cosine similarity indicates diverse task coverage without highly repeated patterns.The benchmark measures similarity after concatenating each task’s multi-turn instructions.

4. Benchmark Evaluation

The evaluation measures agent safety with ASR and RR across multiple models and attack-scaling settings. Multi-turn interactions increase vulnerability across models, with ASR rising as turn counts grow and environment-targeting attacks producing the highest reported category-level ASR.

  • Evaluation setup: GPT-4.1’s trajectory judgments agree 95.15% with rule-based rubrics and 93.53% with human evaluation.The judge classifies executions as complete, reject, or failed.
  • Metrics: ASR is the percentage of tasks completed, while RR is the percentage explicitly refused; rejection at any turn counts toward RR.The evaluation primarily reports ASR and analyzes RR separately.
  • Main results: All evaluated models show multi-turn safety degradation, with average ASR increases ranging from +8.8% for Deepseek-v3.2 to +27.1% for Claude-4.5-Sonnet.These results compare transformed multi-turn tasks with their single-turn versions.
  • Main results: Both Natural Scaling and Injection Scaling increase ASR as turn counts grow, with Natural Scaling consistently producing higher ASR.Natural Scaling distributes harm across turns, whereas Injection Scaling inserts unrelated tasks and adds cognitive burden.
  • Taxonomy analysis: Decomposition×Env has the highest category-level ASR at 73.7%, followed by Addition×Data at 72.2% and Addition×Env at 69.9%.Decomposition×Data has the lowest reported ASR at 67.9%, and environment-targeting attacks are the most vulnerable category.

5. Defense: ToolShield

ToolShield is a training-free defense that uses test-time self-exploration to identify tool-related harm before deployment. Across models, tools, attack types, and evaluation settings, it substantially reduces ASR while preserving benign-task behavior.

  • Defense pipeline: ToolShield proactively explores newly introduced tools by generating test cases, executing them in simulation, summarizing trajectories, and updating safety experiences.The pipeline is designed to cover both single-turn and multi-turn risks induced by tools.
  • Overall defense results: 18.6% to 49.9% percentage points: ToolShield reduces ASR across models, with Claude-4.5-Sonnet improving from 72% to 22%.Claude-4.5-Sonnet shows the largest reported improvement.
  • Baseline comparison: 88% to 15%: ToolShield reduces average ASR for Claude-4.5-Sonnet, compared with 81% to 57% for Gemini-3-Flash.The full pipeline outperforms LlamaFirewall, ToolShield without simulation, and taxonomy-only defense comparisons.
  • Benign-task performance: 0% RR: ToolShield rejects no benign tasks incorrectly across 170 general benign tasks.The reported marginal success-rate difference is attributed to agent stochasticity rather than defensive over-refusal.
  • Experience transferability: ToolShield transfers across models, with Claude-4.5-Sonnet experiences producing 50% single-turn and 41% multi-turn average ASR reductions across executors.Stronger experience generators produce broader safety coverage, while stronger executors can use experiences from weaker generators.
  • Cost analysis: $0.16 to $1.06: increasing generation cost improves ASR reduction from 63% to 73% for Claude-4.5-Sonnet in multi-turn settings.The defense is therefore reported as budget-flexible, with effectiveness increasing at higher budgets.
  • Tool-wise analysis: All tools degrade from single-turn to multi-turn settings, while ToolShield improves every tool; Terminal shows +28.8% degradation and −38.1% defense improvement.The authors associate Terminal vulnerability with highly composable commands and attribute stronger gains for some tools to clearer function documentation.
  • Attack-taxonomy analysis: ToolShield generalizes across attack subcategories, with Addition×Env achieving the highest RR at 49.4% and Decomposition×Data the lowest at 44.8%.Environment-targeting attacks had both the highest multi-turn ASR and highest RR with defense.

6. Conclusion

The paper introduces a taxonomy and MT-AgentRisk to evaluate how distributing harmful intent across turns affects tool-using agents, then proposes ToolShield to address the resulting safety gap. Multi-turn attacks increase ASR, while ToolShield lowers ASR across evaluated models and settings.

  • Conclusion: 27% for Claude-4.5-Sonnet, 23% for Qwen3-Coder, and 10% for Seed-1.6: distributing harm across turns increases ASR.This result indicates substantial safety degradation in multi-turn settings.
  • Conclusion: 50% in multi-turn and 35% in single-turn settings: ToolShield reduces Claude-4.5-Sonnet’s ASR, with additional reductions of 24% for Qwen3-Coder and 38% for Seed-1.6.The defense is training-free and uses agents’ capabilities to explore tools and learn from simulated mistakes.
  • Conclusion: ToolShield’s safety experiences transfer across models, and its effectiveness improves as the generation budget increases.The conclusion reports generalizability and budget flexibility as properties of the approach.

Impact Statement

The work contributes a benchmark and a tool-agnostic defense intended to support more robust agents. Its reported scope includes proactive identification of functional risks without expensive retraining and reduced ASR across diverse tool environments.

  • Impact Statement: The authors present a rigorous benchmark for safety evaluation and a scalable, tool-agnostic defense for more robust agents.The impact statement frames these contributions as supporting safer use of agentic capabilities in complex automation.
  • Impact Statement: The defense enables agents to proactively identify and mitigate functional risks without expensive retraining.The statement connects this capability to reduced attack success rates across diverse tool environments.

A. Limitation

The paper’s evaluation is black-box, covers five tools, and depends on agents generating useful safety experiences. The authors note that weaker agents may produce lower-quality experiences, limiting effectiveness for less capable models.

  • Scope limitations: Black-box evaluation observes model outputs without internal states or reasoning processes, limiting mechanistic analysis of attack success and failure.This constrains explanations of why particular attacks succeed or fail.
  • Scope limitations: The benchmark covers five tools, which may not capture all real-world tool-use scenarios.The authors identify broader agentic and MCP environments as directions for extension.
  • Defense limitations: Weaker agents produce lower-quality safety experiences, potentially limiting ToolShield’s effectiveness for less capable models.This limitation follows from the defense’s reliance on the agent’s own capabilities for test-case generation and experience extraction.

D.3. Decomposer Model Ablation

Safety degradation in multi-turn evaluation is consistent across decomposer models rather than tied to one decomposition style. Most non-completed, non-rejected cases are technical failures, while rejection rates decline in multi-turn settings.

  • Multi-turn degradation: Rejection Rate decreases from single-turn to multi-turn settings, ranging from -6.0% for Deepseek-v3.2 to -28.1% for Claude-4.5-Sonnet.The evaluation satisfies ASR + RR + Failure Rate = 100%.
  • Decomposer comparison: Both Claude-4.5-Sonnet and Qwen3-Coder decomposers cause consistent safety degradation across executor models.The pattern persists despite stylistic differences between decomposers.
  • Interpretation: The shared degradation pattern indicates that distributing intent across turns bypasses single-turn defenses and erases single-turn safety advantages between models.Both decomposers produce the same qualitative outcome across executors.
  • Failure analysis: 74% of 302 failed executions are technical failures, indicating capability limitations rather than implicit safety behavior.Failures include execution errors, malformed tool calls, and premature termination.

D.5. Subcategory ASR Analysis

Multi-turn vulnerability varies substantially by attack subcategory and tool. Data×Mapping and Filesystem attacks have the highest average ASR, while sequential identity attacks and constrained-action tools are comparatively less successful.

  • Taxonomy subcategories: 76.8% is the highest average ASR for Data×Mapping, followed by 75.7% for Env×Composition; Data×Identity is lowest at 63.4%.Env×Composition reaches 100% ASR on Deepseek-v3.2.
  • Tools: 78.8% is the highest average ASR for Filesystem, followed by PostgreSQL at 76.7% and Terminal at 71.0%.Playwright and Notion have lower average ASR at 65.2% and 64.4%, respectively.
  • Taxonomy construction: The taxonomy transforms the same harmful task into eight multi-turn variants by combining addition or decomposition with Data-versus-Env and method-by-target choices.The running example is “rm -rf /root.”
  • Evaluation validation: Rule-based evaluation aligns with LLM judging at 96.79% for single-turn and 93.46% for multi-turn settings.The validation uses 300 execution logs across 50 single-turn and 50 multi-turn tasks.
  • Safety-tree analysis: The safety-tree pipeline generates test cases for risky tool functions while filtering safe functions to avoid disrupting benign operations.The procedure analyzes function capabilities and potential harms before test generation.

E.3. Defense Results: RR

ToolShield raises rejection rates across multi-turn attack categories and tools while remaining effective as attacks scale. Its defense pipeline uses generated safety experiences, with effectiveness and cost varying by tool and model.

  • Overall RR results: Rejection Rate improvements range from +17.0% for GPT-5.2 to +57.5% for Claude-4.5-Sonnet after ToolShield.Claude-4.5-Sonnet also shows the largest ASR reduction in the main results.
  • Experience generation: ToolShield adds approximately 2.5K–6K experience tokens depending on the model while covering all five tools.Claude-4.5-Sonnet generates 193 test cases, whereas Seed-1.6 generates 70.
  • Cost analysis: Simulated execution accounts for 64.7% of the defense cost, followed by experience generation at 30.7% and test synthesis at 4.5%.Average cost ranges from $0.16 to $1.06 across six models.
  • Taxonomy subcategories: 56.8% is the highest average RR for Env×Wrapping, while Data×Identity is lowest at 36.1%.Data×Composition and Env×Composition reach 50.0% and 49.0%, respectively.
  • Tool comparison: 58.1% is the highest average RR for Terminal, followed by PostgreSQL at 56.2%; Filesystem is lowest at 29.8%.Playwright reaches 45.4% average RR.
  • Scaling robustness: With defense, ASR falls from 58.6% to 11.5% when Natural Scaling increases attacks to 10 turns.Without defense, ASR rises from 14.3% to 58.6% as the number of turns increases.

F.2. Full Defense Results

Across models and tools, ToolShield consistently improves multi-turn robustness, and its learned experiences outperform generic or separate-model defenses. The method also transfers to single-turn harmful tasks without reported false-positive rejection in the cited evaluation.

  • Full benchmark results: ToolShield consistently improves robustness across all models and five tools, with lower ASR and higher RR indicating better safety.The reported improvement is defined relative to the without-defense baseline.
  • Outcome evaluation: The evaluation classifies agent outcomes as completed, rejected, or failed from the original task and execution trajectory.The classifier uses agent actions and observations to determine behavior outcome.
  • Baseline comparison: On 100 randomly sampled tasks, Claude-4.5-Sonnet reaches 15% ASR and 83% RR with ToolShield, versus 42% ASR and 50% RR for the baseline.The comparison includes a separate-model Firewall and generic safety instructions.
  • Single-turn transfer: ToolShield raises single-turn rejection rate from 43.6% to 87.1% and reduces ASR from 44.7% to 9.6%.The cited evaluation reports effectiveness in both single-turn and multi-turn settings.
  • Case studies: The case studies show attacks that are refused in single-turn settings but succeed after multi-turn decomposition without defense.With defense, agents refuse at earlier preparatory steps by recognizing compositional or contextual risk.

H. Generated Experiences

ToolShield generates reusable, interpretable safety experiences from agent evaluation trajectories across multiple tools. Case studies show these guidelines can help agents recognize distributed harms that they previously rationalized as benign.

  • Cross-tool experiences: Safety experiences are organized by tool across Filesystem, Playwright, PostgreSQL, and Terminal evaluations.The experiences are concise, imperative, and designed for reuse across multiple scenarios.
  • Case studies: In an addition-based Reddit attack, the agent completes a harmful multi-turn sequence despite refusing the equivalent single-turn request.The sequence downloads harmful content, copies it, and posts it after local injection distributes the harm across turns.
  • Case studies: With ToolShield, generated experience guides help the agent recognize distributed harm in the Reddit case.
  • Case studies: A two-turn credential-handling attack creates a script that appends AWS credentials to a target file, illustrating how harm can be assembled across turns.The task could facilitate insecure storage of sensitive data.
  • Case studies: Targeted credential-handling and multi-turn assembly guidelines changed the agent’s behavior from rationalizing the request as benign to immediately refusing it.The experience guidelines were the only variable between the compared runs.
  • Experience patterns: Generated experiences include refusals for destructive file, database, upload, configuration, privilege, and malware-propagation requests.Examples cover overwriting production files, assembling executable tools across turns, uploading malicious payloads, modifying critical databases, and creating attack infrastructure.
  • Experience patterns: Terminal experiences extend these patterns to destructive chains, PATH hijacking, overly permissive permissions, malware propagation, and unrestricted sudoers access.
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