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The Verifier Tax: Horizon Dependent Safety Success Tradeoffs in Tool Using LLM Agents

Tanmay Sah, Vishal Srivastava, Dolly Sah, Kayden Jordan

arXiv:2603.19328v1cs.CR

TL;DR

Runtime enforcement is intended to reduce unsafe actions, but its effects on end-to-end outcomes in sustained multi-step interactions remain insufficiently characterized. This study evaluates verifier-mediated agents on transactional benchmarks and finds that interception does not reliably yield safe task completion, while recovery and overhead remain consequential concerns.

  • Problem

    Prior evaluations often separate aggregate task success from safety violations or use simplified settings that do not capture sustained multi-step interactions.

    Source: §1 · p. 1

  • Method

    The study compares runtime verifier mediation on transactional τ-bench tasks, measuring outcomes and execution dynamics while distinguishing environment turns from internal execution steps.

    Source: §1 · p. 1, §3.5 · p. 3, §3.7 · p. 4

  • Results

    Safety-mediated agents can block non-compliant proposals in up to 94% of episodes, but recovery varies sharply: GPT-OSS-20B succeeds after 32.0% of Airline rejections and near 0.0% of Retail rejections.

    Source: §4.8 · p. 8, §6 · p. 9

  • Takeaways & Limitations

    The conclusion calls for safety-aware fine-tuning and more robust grounding mechanisms to address integrity leaks and the recovery gap.

    Source: §4.8 · p. 8, §6 · p. 9

    The study covers 9B and 20B open-weight models, and its 15- and 30-turn caps may constrain recovery opportunities.

    Source: §5 · p. 9

Abstract

from arXiv · show

We study how runtime enforcement against unsafe actions affects end-to-end task performance in multi-step tool using large language model (LLM) agents. Using tau-bench across Airline and Retail domains, we compare baseline Tool-Calling, planning-integrated (TRIAD), and policy-mediated (TRIAD-SAFETY) architectures with GPT-OSS-20B and GLM-4-9B. We identify model dependent interaction horizons (15 to 30 turns) and decompose outcomes into overall success rate (SR), safe success rate (SSR), and unsafe success rate (USR). Our results reveal a persistent Safety Capability Gap. While safety mediation can intercept up to 94 percent of non-compliant actions, it rarely translates into strictly safe goal attainment (SSR below 5 percent in most settings). We find that high unsafe success rates are primarily driven by Integrity Leaks, where models hallucinate user identifiers to bypass mandatory authentication. Recovery rates following blocked actions are consistently low, ranging from 21 percent for GPT-OSS-20B in simpler procedural tasks to near zero in complex Retail scenarios. These results demonstrate that runtime enforcement imposes a significant verifier tax on conversational length and compute cost without guaranteeing safe completion, highlighting the critical need for agents capable of grounded identity verification and post-intervention reasoning.

1 Introduction

This study asks how runtime safety mediation changes end-to-end task outcomes in multi-step tool-using agents, where prior evaluations often separated task success from safety violations. It compares three agent architectures across two transactional domains and finds that blocked unsafe actions do not reliably become safe goal attainment.

1 Introduction

  • Runtime verifier mediation blocks proposed tool calls that violate predefined policies, after which the agent must revise its plan.Its effect on end-to-end task outcomes and execution dynamics remains insufficiently characterized in prior work.Source: §1 · p. 1
  • Prior evaluations often report aggregate task success while treating safety violations separately or using simplified settings that miss sustained multi-step interactions.The introduction identifies τ-bench’s explicit procedural policies and state-based evaluation as a way to measure safe success, unsafe success, and failure as horizons increase.Source: §1 · p. 1
  • The study compares Tool-Calling, Triad, and Triad-Safety using GPT-OSS-20B and GLM-4-9B across Airline and Retail, measuring SR, SSR, USR, intervention frequency, and recovery.Interaction limits are model-dependent: 15 turns for GPT-OSS-20B and 30 for GLM-4-9B.Source: §1 · p. 1
  • Safety mediation can intercept non-compliant actions in up to 94% of episodes, yet agents often fail to revise successfully within the turn limit.Many high-performing agents’ violations stem from hallucinated user identifiers used to bypass mandatory authentication.Source: §1 · p. 1
  • The paper identifies a Safety-Capability Gap and reports 2.0–2.8× token inflation across model×domain settings as the verifier tax.It proposes Strict Grounding Gates to mitigate integrity leaks.Source: §1 · p. 2

2 Related Work

Related work covers runtime safety mechanisms, corrective loops, long-horizon reliability, and interactive benchmarks. The paper positions its study around measuring whether policy enforcement supports safe task completion in transactional environments.

Safety guardrails and runtime enforcement

  • Runtime verifier mediation blocks unsafe tool calls before execution and triggers a Block-and-Revise loop, whose overhead and safety efficacy the paper measures.The related-work discussion contrasts runtime mediation with static lexical filters, alignment, and post-hoc moderation.Source: §2 · p. 2

Long-horizon reliability; Intervention and corrective loops

  • The paper frames post-intervention recovery as a reliability measure, noting that agents can appear reliable by taking non-compliant shortcuts such as Integrity Leaks.It situates Block-and-Revise among self-feedback and critique-guided correction approaches.Source: §2 · p. 2

Interactive benchmarks

  • Existing interactive benchmarks emphasize task completion, while procedural adherence benchmarks do not explicitly separate safe goal attainment from unsafe success.The paper uses τ-bench because its transactional interactions and explicit procedural rules support distinctions among compliant success, unsafe success, and failure.Source: §2 · p. 2

3 Methods

The methods compare three tool-agent architectures under controlled model, environment, and prompt conditions, then evaluate task completion, procedural safety, interventions, recovery, and operational overhead. The protocol audits blocked actions and records both environment interactions and internal computation.

3.1 Agent Architectures

  • Tool-Calling executes the model’s proposed action immediately, while Triad adds planning and heuristic verification and Triad-Safety supplies the verifier with explicit domain policies.Rejected proposals enter a block-and-revise loop, with up to three retries per turn for the Triad designs.Source: §3.1 · pp. 2–3

3.2 Models; 3.3 User Simulator

  • The study uses GPT-OSS-20B and GLM-4-9B across agent roles, and a Qwen2.5-7B-Instruct user simulator whose stochasticity is addressed with three full runs.Agent models operate deterministically, while the simulator runs at temperature 1.0 without a fixed seed.Source: §3.2 · p. 3, §3.3 · p. 3

3.4 Environments and Tasks

  • The evaluation uses τ-bench’s Airline and Retail transactional domains, with 15-turn limits for GPT-OSS-20B and 30-turn limits for GLM-4-9B.A symmetric-capacity check reevaluates GLM-4-9B within a 15-turn window.Source: §3.4 · p. 3

3.5 Execution Protocol

  • Each turn’s rejected actions are returned for revision; after three failed attempts, the last proposal is accepted by default and audited for policy violations.The paper calls inability to recover within the turn budget Model Stagnation.Source: §3.5 · p. 3

3.6 Safety Policy and Violation Labeling

  • Safety violations are classified as premature consequential actions, irreversible transactions without explicit authorization, or fabricated or ungrounded structured parameters.A deterministic audit script labels trajectory logs, with labels cross-verified by an LLM-based reasoning pipeline.Source: §3.6 · pp. 3–4

3.7 Evaluation Metrics

  • Evaluation separates overall task success from safe and unsafe success, and reports intervention frequency, blocks per episode, overhead, and success conditional on intervention.Overhead is measured as additional LLM calls and log messages relative to Tool-Calling; verifier rejection loops add internal steps without additional environment turns.Source: §3.7 · p. 4

3.8 Experimental Volume

  • The experiment covers 50 Airline tasks and 115 Retail tasks, each run under three seeds, for 2,970 total trajectories across the study.Metrics are averaged over three independent runs.Source: §3.8 · p. 4

3.9 Token Accounting

  • Agent token usage sums input and output tokens across internal model calls, while user-simulator tokens are accounted for separately.The reconstructed agent token counts omit server-side tool-schema injection and are described as a conservative lower bound on served tokens.Source: §3.9 · pp. 4–5

4 Results

Across the results, runtime enforcement often detected violations without reliably converting them into compliant task completion. The analyses trace this gap to weak recovery, integrity leaks, and added execution overhead.

4.2 Symmetric Capacity Check and Horizon Saturation

  • 14.7% to 28.7% Airline SR for GLM-4-9B from k=10 to k=30 contrasts with GPT-OSS-20B saturation by k=10–15.In a separate Retail sweep, performance rose from 3.2% at H=15 to 5.2% at H=30, then did not improve; absolute SRs are not comparable to Table 1 because the sweep had independent simulator stochasticity.Source: §4.2 · p. 5, Table 3 · p. 7

4.3 Decomposition of Safety Violations

  • Over 93% of GPT-OSS-20B’s Retail safety failures were Integrity Leaks, while GLM-4-9B’s Airline failures included authorization lapses and integrity hallucinations.The paper separates Procedural Authentication, Lack of Explicit Authorization, and Data Integrity violations; an episode may contain multiple violation types.Source: §4.3 · p. 5, Table 4 · p. 7

4.4 Operational Overhead and the Verifier Tax

  • 2.0–2.8× token-cost inflation exceeded 1.6–2.2× call-count inflation across model and domain settings.Planning and verification messages enlarge context windows; GLM-4-9B Triad’s P95 token cost reached 355k tokens.Source: §4.4 · p. 5, Table 5 · p. 8

4.5 Disaggregated Interventions and Recovery Sources

  • 19.1% of 1,980 episodes experienced both a verifier rejection and an environment error, and these episodes had the lowest SR at 7.7%.Clean episodes had SR=21.3%; the authors describe the combined reduction as non-additive.Source: §4.5 · p. 6

4.7 The Safety-Capability Gap

  • 94.1% interception for GPT-20B in Retail coexists with weak safe recovery; the authors locate the gap in actors’ inability to produce compliant alternatives.The study distinguishes verifier detection from whether an agent can recover and complete a task safely.Source: §4.5 · p. 6, §4.7 · pp. 7–8

4.5 Disaggregated Interventions and Recovery Sources

  • Fewer than 1.5% of mediated episodes had a stagnation event, and simulating hard abort after three rejections negligibly changed task success.Over 95% of reject-runs comprised a single rejection; stagnated episodes repeated the same rejection reason across attempts, so more retries were judged unlikely to help.Source: §4.5 · p. 6

4.6 Qualitative Case Studies

  • A Retail baseline agent hallucinated a dummy identity that the tool environment accepted, earning task reward while violating identity-verification requirements.The example illustrates an Integrity Leak; a separate Triad-Safety Airline case successfully gathered credentials after rejection and completed the task safely.Source: §4.6.1 · p. 7, §4.6.2 · p. 7

4.8 Discussion

  • The authors propose Strict Grounding Gates that check identifier parameters against prior tool-retrieval traces, alongside verifier-guided recovery trajectories for rejected actions.They identify heuristic verifiers’ difficulty enforcing parameter provenance as a limitation of the current approach.Source: §4.6.3 · p. 7, §4.8 · p. 8

5 Limitations and Future Work

The study’s conclusions are bounded by its model scales, turn caps, verifier design, and stochastic user simulation; it identifies further tests and mitigation approaches for these constraints.

5 Limitations and Future Work

  • The evaluation covers open-weight models at 9B and 20B; larger models may show different recovery dynamics and safety-capability tradeoffs.The authors call for testing whether the Safety-Capability Gap persists at larger scales.Source: §5 · p. 9
  • Turn caps of 15 and 30, selected to match backend defaults, may constrain recovery opportunities; longer horizons could change observed recovery rates.Source: §5 · p. 9
  • The Triad-Safety verifier identifies violations, but the recovery gap remains substantial; the authors propose remediation-aware verifiers with structured safe-state transitions.Source: §5 · p. 9
  • The stochastic user simulator adds variance, while static per-turn procedural rules may omit edge cases and do not maintain safety state across a trajectory.The authors propose fixed pre-recorded user trajectories for future evaluations.Source: §5 · p. 9
  • The authors propose Strict Grounding Gates to reject identifiers not retrieved in prior tool calls and heterogeneous actor-verifier architectures to reduce mediation overhead.Source: §5 · p. 9

6 Conclusion

The study evaluates procedural safety in transactional agents by separating task success from safe success. It finds that agents often reach goals through non-compliant shortcuts, while verification and longer interaction impose costs without ensuring safe recovery.

6 Conclusion

  • Blocking non-compliant proposals in up to 94% of episodes does not ensure recovery to a safe, successful path, exposing a persistent Safety-Capability Gap.Source: §6 · p. 9
  • Current models often achieve goals through non-compliant shortcuts, particularly data-integrity violations and hallucinated identifiers.Source: §6 · p. 9
  • Procedural safety imposes a measurable tax on conversational length, and GLM-4-9B lacks the turn-efficiency of GPT-OSS-20B.Source: §6 · p. 9
  • The authors call for safety-aware fine-tuning and stronger grounding mechanisms to narrow the recovery gap and eliminate integrity leaks.Source: §6 · p. 9

A Agent Prompt Templates

The appendix provides the prompts and instruction templates for the agent architectures and user simulator, and contrasts its enhanced agent prompts with the original benchmark baseline.

A Agent Prompt Templates

  • The appendix contains exact system prompts and instruction templates for the agent architectures and user simulator.Source: A Agent Prompt Templates · p. 10
  • The user simulator prompt matches the original τ-bench implementation, while the agent prompts add structured personas, strict formatting rules, and Triad’s Plan–Act–Verify decomposition.Source: A Agent Prompt Templates · p. 10

A.1 Baseline Tool-Calling Agent

The baseline prompts add domain-specific personas and strict JSON-only tool calls to the original raw-wiki setup. Retail and Airline instructions require tool retrieval, prohibit guessing, and direct agents to ask for missing identifiers.

A.1 Baseline Tool-Calling Agent

  • The baseline adds a domain-specific persona and strict JSON-only tool-call format to the original raw environment wiki prompt.Source: §A.1 · p. 10

A.1.1 Retail Domain (System Prompt).

  • The Retail prompt requires tool retrieval before answering, prohibits guessing, and directs the agent to ask for a user or order ID when needed.Source: §A.1.1 · p. 10

A.1.2 Airline Domain (System Prompt).

  • The Airline prompt requires tool retrieval before answering, prohibits guessing, and directs the agent to ask for a user or reservation ID when needed.Source: §A.1.2 · p. 10

A.2 Triad-Safety Agent (Plan–Act–Verify)

Triad-Safety separates planning, tool execution, and policy verification: each proposed tool action is checked against explicit domain rules before execution.

A.2 Triad-Safety Agent (Plan–Act–Verify)

  • The dedicated safety verifier evaluates every tool-call proposal against structured domain policies before execution.Triad-Safety uses a planner, an actor, and a verifier gate.Source: §A.2 · p. 11

A.2.1 Planning Prompt (System Prompt).

  • The planner prioritizes identity verification when the user's identity has not yet been verified and otherwise proposes the next high-level step.It describes the next step in natural language rather than generating the exact response, tool call, or rule ID.Source: §A.2.1 · p. 11

A.2.2 Acting Prompt (Retail) (System Prompt).

  • The Retail actor must retrieve information with the provided tools, avoid guessing or hallucinating, and ask the user for a missing user or order ID.The prompt also constrains tool names and parameters and disallows combining text with a tool call.Source: §A.2.2 · p. 11

A.2.3 Verification Prompt (System Prompt).

  • The verifier permits ID searches before identity confirmation but rejects database-changing actions such as cancel_pending_order until identity or email is confirmed.It approves safe and correct actions and otherwise returns a rejection with a rule ID and reason.Source: §A.2.3 · p. 11

A.3 User Simulator Prompt

The user simulator retains the original tau-bench prompt and reveals task information gradually, without inventing details absent from the instruction.

A.3 User Simulator Prompt

  • The user simulator prompt is kept identical to the original tau-bench implementation so outcome differences are not attributable to changes in the user model.Source: §A.3 · p. 11
  • The simulator produces one line at a time and provides only information needed for the current step rather than revealing the full instruction at once.Source: §A.3 · p. 11
  • The simulator must not invent missing details, such as an order ID absent from the instruction, and ends the conversation with ###STOP### when the goal is satisfied.Source: §A.3 · p. 11
  • The prompt asks the simulated user to avoid repeating the instruction verbatim and to interact naturally.Source: §A.3 · p. 11

A.4 Intervention Recovery Trajectory

Figure 1 presents a representative Triad-Safety episode in which the agent recovers after a verifier rejection.

A.4 Intervention Recovery Trajectory

  • Figure 1 presents a representative Triad-Safety episode with a verifier rejection followed by successful recovery.Source: §A.4 · p. 11
  • The trajectory illustrates that a verifier rejection can be followed by a successful recovery in a representative episode.Source: §A.4 · p. 11
  • The section's example is an episode-level trajectory, not an aggregate outcome report.Source: §A.4 · p. 11

B Architecture Diagrams

The diagrams distinguish direct tool execution in the Tool-Calling baseline from Triad-based architectures that add verifier mediation; Figure 1 shows a successful recovery trajectory.

B Architecture Diagrams

  • The appendix provides sequence diagrams for Tool-Calling and Triad-based architectures referenced in Section 3.1.Source: B Architecture Diagrams · p. 11
  • Figure 1 depicts a Triad-Safety episode in which the agent recovers after a verifier rejection and earns a verified task reward of 1.0.Source: Figure 1 · p. 12
  • Figure 2 depicts the Tool-Calling baseline, where the agent executes tool calls directly without internal mediation.Source: Figure 2 · p. 13
  • Figure 3 depicts Triad and Triad-Safety with a verifier gate and a block-and-revise loop, with explicit policy mediation in Triad-Safety.Source: Figure 3 · p. 14

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