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Agents Trust Tools Too Much: Measuring Reliance on Unreliable Tools

Hoyeol Yang, Woojung Song, Taewon Kim, Jonghyun Song, Seoyeon Park, Yohan Jo

arXiv:2609.05587v1cs.AI

TL;DR

Tool-use evaluations often assume reliable returns, but agents may adopt plausible corrupted outputs. This paper evaluates fourteen models across search, sub-agent delegation, and code execution, finding persistent overtrust and inconsistent mitigation.

  • Problem

    Existing evaluations largely assume reliable tool returns, leaving limited evidence about when agents trust plausible but incorrect outputs and how that reliance can be mitigated.

  • Method

    The study corrupts returns from web search, LLM sub-agent delegation, and code execution, then measures Adoption and Override rates and tests prompting, metadata, and post-training interventions.

  • Results

    Across models and tools, overtrust remains common: mean Adoption rate exceeds one third for every tool and reaches 68.0% for Search.

  • Takeaways & Limitations

    Agents may recognize conflicts internally yet present corrupted answers without warning, and no tested intervention consistently reduces overtrust across tools.

  • Takeaways & Limitations

    In sub-agent summarization, summaries are generated from titles rather than source text, and the receiving agent cannot independently check the delegate.

Abstract

from arXiv · show

Existing evaluations of tool-using agents primarily measure whether an agent can successfully complete diverse tasks with tools. These evaluations generally assume that tools return reliable information. However, tool returns in real-world systems can be plausible yet incorrect. We investigate how agents respond to unreliable tool returns by evaluating fourteen LLMs using three tools-web search, LLM sub-agent delegation, and code execution. For each tool, we corrupt its returns and measure whether agents adopt the corrupted content in their final answers. Agents exhibit high levels of overtrust across all three settings: the mean adoption rate exceeds one third for every tool and reaches 68.0% for web search. Analysis of reasoning traces reveals a particularly concerning failure mode: agents often recognize conflicts and even recover the correct answer internally, yet present only the corrupted answer without warning the user. To mitigate agents' overtrust in tool returns, we intervene at three levels: prompting by the user, metadata from the tool provider, and post-training by the agent builder. Although some interventions help for particular models or tools, none consistently mitigates overtrust across tools. These findings identify overtrust in unreliable tools as a serious and persistent failure mode, motivating evaluations and interventions that enable agents to validate tool outputs and transparently communicate unresolved conflicts.

1 Introduction

The paper examines whether tool-using agents adopt plausible but corrupted tool returns, extending evaluations beyond the usual assumption of reliable tools. It measures adoption, answer overrides, and mitigation across web search, sub-agent delegation, and code execution.

  • Existing tool-use evaluations largely assume reliable returns, despite generated search answers, delegated reports, and model-generated code outputs being plausibly incorrect.
  • The study asks whether agents carry corrupted content into final answers, how plausibility changes adoption, and which interventions reduce reliance.
  • Fourteen models are tested with web search, an LLM sub-agent, and a code executor after only the returned content is corrupted.
  • Mean Adoption rate exceeds one third for every tool and reaches 68.0% for Search, while Override rate remains substantial.
  • Only 5.3% of Search and 1.8% of Code Executor final answers that adopt corrupted content warn users.
  • Prompting, tool metadata, and post-training help selectively but none consistently mitigates overtrust across models and tools.

2 Related Work

Prior work studies conflicts in retrieved or noisy contexts, but tool-using agents additionally decide whether and when to invoke tools. This paper compares reliance across tool types and distinguishes adoption from tool-induced overrides, conflict recognition, disclosure, and mitigation.

  • Knowledge-conflict studies mainly place contradictory retrieved passages directly in the model’s prompt rather than evaluating tool-use decisions.
  • Standard tool-use benchmarks measure tool selection, invocation, and task completion with correct returns, not behavior when returns are incorrect.
  • The paper compares reliance across search, code execution, and sub-agent delegation instead of treating performance degradation as a single outcome.
  • It distinguishes general adoption from cases where corrupted tool content overrides an answer the model had already produced correctly without a tool.
  • The study also tests whether agents recognize and disclose conflicts and whether user instructions, tool metadata, or post-training mitigate reliance.

3 Evaluation Design

The evaluation measures agents’ reliance on corrupted returns across three tool-specific tasks using Adoption and Override rates. It varies corruption plausibility, item types, and model configurations across web search, sub-agent summarization, and code execution.

  • The study evaluates question answering with web search, document summarization with an LLM sub-agent, and mathematical problem solving with a code executor.
  • Adoption rate measures use of injected content in the final answer, while Override rate measures adoption on items answered correctly without a tool.Base accuracy B is the proportion of items judged correct by majority vote over three independent no-tool responses.
  • Web search: Web-search evaluation uses 418 FreshQA questions divided into never-, slow-, and fast-changing items, with fixed alternatives replacing gold answers in returned snippets.
  • LLM sub-agent: Sub-agent evaluation uses 45 documents across pre-cutoff papers, nineteenth-century novels, and post-cutoff papers, with false claims or fabrications inserted into summaries.
  • Code executor: Code-executor evaluation uses 300 arithmetic problems spanning addition, multiplication, division, modulo, logarithm, and exponentiation, then corrupts captured numeric outputs.
  • Corruption design: Corruption magnitude p is sampled log-uniformly from [1, 100] for P1 and [101, 10000] for P2, while direction and sign are independently sampled from {+1, −1}.
  • Models: The evaluation covers fourteen models, including Qwen3, Gemini, and Muse-Glimmer configurations.

4 Results

Across web search, sub-agent delegation, and code execution, agents frequently adopt corrupted tool returns, including when they can already answer correctly. They sometimes detect conflicts internally but still present the corrupted answer without warning the user.

  • Adoption and Override Across Tools: 68.0% Search Adoption for plausible P1 corruptions, with mean Adoption above one third in every tool setting.Code Executor models adopted more than one third of P1 returns despite roughly 85% no-tool accuracy; even unrelated P2 values were adopted at 12.6% in Search and nearly one quarter in sub-agent and executor settings.
  • Adoption and Override Across Tools: Override remains above one half in Search and near one third in the Code Executor, including 33.4% alongside 35.4% Adoption in the executor.Override measures corrupted returns changing answers that were already demonstrated correct; it declines for less plausible corruptions but remains substantial.
  • Model Differences: No model family is consistently resistant, although Gemini, Gemma, and Muse generally adopt less than GPT-family models in the Code Executor.Thinking lowers Qwen’s P1 Adoption in every setting, but thinking models still adopt most P1 Search returns; model-size effects are inconsistent.
  • Item-Type Effects: 59.7% Search Adoption for never-changing facts rises to 76.6% for fast-changing facts, while Override remains 53.9–56.1% across groups.The substantial rate for never-changing facts is difficult to explain as reasonable reliance on current information.
  • Item-Type Effects: 56.4% Adoption occurs when Code Executor corruption preserves the sign, versus 14.3% when it inverts the sign; Override is 51.0% versus 15.1%.Models reject most sign-inverted values but adopt more than half of sign-preserving corruptions, even after demonstrating the correct answer.
  • Detecting but Not Disclosing Conflicts: Pooled warning rates are 5.3% in Search and 1.8% in the Code Executor, despite additional calls and frequent internal conflict recognition.In executor traces, more than 90% of adopting Qwen traces mention a conflict, and many state the correct value, yet final answers usually select the corrupted value without warning.

5 Can Tool Reliance Be Shifted?

The study tests whether changing information presentation, user instructions, provider metadata, or post-training can reduce reliance on corrupted tool returns without sacrificing accuracy. Interventions help selectively, but no approach consistently mitigates overtrust across channels.

  • 5 Can Tool Reliance Be Shifted?: Interventions compare tool, user, and RAG presentation routes, plus prompt, metadata, and post-training changes, using P0 accuracy and P1 corrupted-return Adoption.The evaluation defines a useful intervention as one that lowers P1 Adoption without lowering P0 accuracy.
  • 5.1 Presenting External Information: 39.9% Adoption occurs when a corrupted number comes from the Code Executor, compared with 1.8% from a user and 3.8% through RAG.Search and sub-agent channels show smaller route differences, indicating that presentation effects depend on the tool channel.
  • 5.2 Prompt Intervention: Prompt policies reduce P1 Adoption selectively: DISCLOSE lowers Search Adoption by 5.2 points, VERIFY lowers Sub-Agent Adoption by 6.0 points, and COMPARE or VERIFY lower Code Executor Adoption by more than eight points.COMPARE produces the largest average reduction in each channel but lowers mean P0 accuracy in Search and Sub-Agent.
  • 5.2 Prompt Intervention: No prompt policy consistently lowers P1 Adoption while preserving P0 accuracy across settings.The best policy depends on the channel, and some policies increase Adoption at the model level.
  • 5.3 Metadata Intervention: Provider metadata provides no consistent mitigation; instructing models to consider higher reliability raises Adoption by 3.7 points for numeric labels and 4.5 points for categorical labels.Every instructed metadata condition has higher mean Adoption than the P1 reference, although effects vary by model.
  • 5.4 Post-Training Interventions: Fixed-context DPO reduces Search P1 Adoption by 5.1 points and Override by 7.8 points, but costs 1.7 points of P0 accuracy and leaves Executor Adoption unchanged.Recovery SFT changes Search P1 Adoption by only 1.1 points, while Correct-tool SFT increases Adoption in both channels.

6 Conclusions

The paper identifies persistent overtrust in corrupted tool returns across Search, Sub-Agent delegation, and Code Executor settings. Agents may detect conflicts internally yet still present corrupted answers without disclosure, while interventions do not reliably solve the problem.

  • 6 Conclusions: Agents carry plausible but corrupted tool returns into final answers even when they could otherwise answer correctly.Adoption measures carrying corrupted content, while Override captures cases where corruption changes an answer already produced correctly without a tool.
  • 6 Conclusions: Agents often make additional calls or recognize conflicts in reasoning, yet rarely disclose those conflicts when adopting corrupted returns.This separates internal conflict detection from transparent final reporting.
  • 6 Conclusions: Changing prompts helps selected models and channels, but higher stated reliability can make corrupted returns more persuasive and post-training does not reliably transfer across channels.The conclusion favors governed reliance rather than reducing tool use.
  • 6 Conclusions: Evaluation should jointly measure resistance to corrupted returns, accuracy with uncorrupted returns, and disclosure of conflicts already recognized by the model.The paper recommends independent validation of tool returns and communicating unresolved conflicts to users.

A Web Search: Dataset and Protocol

The web-search evaluation uses frozen FreshQA questions and fixed corrupted alternatives, with reproducible caching, exposure filtering, and judge-based labeling. Its protocol distinguishes plausible, transplanted, and nonexistent values while noting that serving asymmetries limit cross-model comparisons.

  • Dataset: 418 FreshQA questions remain after excluding 14 binary items from the original 432-question pool.The set contains 147 never-, 127 slow-, and 144 fast-changing items.
  • Corruption conditions: P1 is a credible real value, P2 is another question’s gold answer, and P3 is a nonexistent value.P1 sources include earlier FreshQA answers, verified alternatives, and manual completion; P2 assignments are deterministic.
  • Search serving: The editor rewrites claim-bearing titles and snippets to assert the target value while preserving relevance scores and screening for gold-answer leaks.Leaking URL slugs are rewritten or dropped after failed repair attempts.
  • Search serving: Frozen query caching returns identical edited results across repeated evaluations and models on exact-query hits.Offline replay is byte-for-byte on exact hits, but fallback retrieval is not live search and was not used for reported collection misses.
  • Evaluation controls: Exposure filtering retains 46,184 runs, while 4,066 runs made no tool call and were excluded from the primary denominator.A stricter no-gold gate retains 41,216 runs.
  • Evaluation controls: Cross-model rankings are constrained by differing output budgets, serving precision, and recollection procedures across API and local cohorts.Within-model contrasts are less exposed to these asymmetries than between-model rankings.

B.5 Baseline and judge checks

The sub-agent baseline compares closed-book accuracy across document groups and checks whether apparent post-cutoff knowledge reflects title cues. Baseline and delegate adoption rates are also reported for claim pairs and compatible claims.

  • Judge checks: 1.62% no-tool and 4.14% accurate-delegate apparent Adoption rates include compatible or nested claims, while mutually exclusive pairs occur at 0.05% and 0.09%.The mutually exclusive figures are reported separately from the remaining compatible or nested cases.
  • Baseline: 56.0–62.7% accuracy on pre-cutoff papers contrasts with 14.2–15.2% on post-cutoff papers in the closed-book baseline.The baseline supplies the Base Acc. column of Table 1.
  • Baseline: 33 of 51 post-cutoff closed-book successes come from two documents whose titles state their main claims.Therefore, title-revealing items do not establish that the model read or memorized the document.
  • Protocol boundary: Novel summaries are generated from titles rather than source text, leaving the receiving agent without an independent route to check the delegate.The novel P0 condition is defined by generator output.

C Code Executor: Dataset and Protocol

The code-executor evaluation freezes arithmetic problems and per-problem perturbations, then measures whether agents adopt scaled or sign-inverted outputs. It uses rule-based final-answer grading and reports substantial Override rates across tools and corruption levels.

  • Dataset: 300 problems cover six operations with 50 items each, using frozen generated problems and gold answers computed with 50-digit decimal arithmetic.Valid-domain constraints and three-decimal half-up rounding define the benchmark outputs.
  • Corruption protocol: The executor multiplies every numeric token in successful output by a frozen factor, while failed executions return their traceback unchanged.The factor varies by magnitude, direction, and sign conditions.
  • Corruption protocol: The factor is fixed per problem, so repeated calls return the same scaled value.Candidates are redrawn when perturbation rounds to the correct answer at three decimals.
  • Run protocol: All fourteen models complete three repetitions, with tool-use rates of 83–100% and successful-exposure conditioning changing Adoption by at most 2 points.Each repetition schedules 900 runs across the three conditions.
  • Judging: Rule-based grading marks a response adopted when its final value matches the injected value or another corrupted number returned in that run.Parse failures remain in the denominator.
  • Results: 20.0% is the mean P2 Override rate for the sub-agent, compared with 22.7% for the executor and 6.6% for Search.The mean Search P3 Override rate is 26.8%.
  • Results: 49.2–59.3% target-exposed P1 Adoption and 33.3–40.0% single-run Override occur in an exploratory 90-item Search extension.Its single-run Override measure is not directly comparable to the three-repetition measure.

D.3 Item-type results by model

The item-type analyses organize Adoption and Override results by the characteristic relevant to each tool, while documenting denominator differences and warning-rate summaries. Search adoption rises with answer volatility, but warning behavior remains uncommon in aggregate.

  • Metric structure: Adoption and Override are separated in the breakdowns, with Override restricted to demonstrated-correct items and potentially smaller denominators.Pooled rows combine model–item runs within each corresponding group.
  • Breakdowns: The tables report Search results by volatility, sub-agent results by document group, and executor results by operation and sign condition.A separate table reports sub-agent call ratios and warning rates by model.

E Statistical Checks

The paper reports uncertainty intervals and paired statistical contrasts for reliability checks and intervention analyses. These procedures preserve item-level dependence and distinguish model- and condition-specific differences.

  • Statistical uncertainty: 2,000 item-bootstrap resamples produce confidence intervals while retaining all runs associated with each sampled item.Table 1 reports intervals for model-averaged estimates.
  • Search gate check: 57.8% is the strict no-gold-gate mean P1 Override rate for Search, compared with 56.4% under the primary gate.The largest model-level change between gates is 3.2 percentage points.
  • Executor contrasts: Qwen3-30B shows instruct-minus-thinking differences of +12.8 points for P1 and −4.1 points for P2 in the Executor.The corresponding Qwen3-235B differences are +18.7 and +10.7 points.
  • Executor contrasts: The Qwen3-30B P2 contrast has p = 0.08, whereas the other three reported contrasts have p ≤10−4.These contrasts use paired item differences and sign-flip tests.
  • Intervention contrasts: Intervention analyses report paired intervention-minus-reference differences with 95% item-bootstrap intervals.For Sub-Agent evaluations, the document is the resampling unit; tables provide pooled, model-level, metadata, and post-training intervals as appropriate.

F Intervention Protocols

The intervention protocols standardize corrupted returns across presentation, prompting, metadata, and post-training conditions. Frozen payloads, fixed item slices, repeated prompts, and controlled training data support paired comparisons.

  • Presentation conditions: Search, Sub-Agent, and Code Executor User and RAG conditions reuse frozen corrupted returns while changing how the information is presented.RAG places the same information under a Context header, whereas User conditions present it in natural-language prompts.
  • Prompt interventions: 60 fixed items per channel and six models form the prompt-intervention evaluation.COMPARE, VERIFY, and DISCLOSE each add one conflict-handling instruction, while PLAIN reuses canonical results.
  • Prompt interventions: P0 is the uncorrupted correct-return control, while P1 denotes adoption of a corrupted return in the intervention tables.The prompt protocol uses fixed repetitions and cached Search resolution without live API calls.
  • Metadata interventions: Metadata interventions append structured source or reliability fields to P1 returns and optionally add a selection instruction to the system prompt.The metadata evaluation uses the same six models and fixed 60-item Search slice.
  • Post-training interventions: Recovery SFT, Correct-tool SFT, and Fixed-context DPO use 258, 194, and 327 training examples or preference pairs, respectively.All three interventions use rank-32 LoRA adapters over the same base model.

G Data Integrity and Exposure Audits

The audit documents forced finalization, missing or invalid runs, cache timing, output-budget effects, payload joins, and injected-result handling. These checks define how exposure and failures enter the reported analyses.

  • Execution handling: Forced finalization adds a final-answer request with tools disabled to every Code Executor episode reaching the turn cap.This includes 19 DeepSeek-V4-Flash cases.
  • Execution handling: 0.8, 0.2, and 0.9 points are the increases in DeepSeek-V4-Flash P1 Adoption, P2 Adoption, and P1 Override rates under forced finalization.The increases correspond to the three metrics in that order.
  • Search cache timing: 51.5% and 50.9% are the live-resolution rates for Gemini-3.7-flash and Muse-Glimmer-30B collected after the initial Search cache freeze.Resolved results were frozen immediately and remaining calls replayed the existing cache.
  • Output-budget caps: 8.2%, 3.2%, and 0.7% of closed-book runs for GPT-OSS-20B, GPT-5.4-mini, and GPT-5.4-nano exhaust the 16k output budget without a final answer.These runs count as incorrect, and no other eleven models are affected.
  • Delivered-payload audit: The Search manifest contains 75,625 rows, with 75,621 joining uniquely and four failing closed.The primary gate retains 15,455, 15,387, and 15,342 P1, P2, and P3 runs, respectively.
  • Delivered-payload audit: The absolute Adoption-rate differences between primary and strict no-gold gates are at most 3.4, 1.9, and 6.7 points for P1, P2, and P3.The strict no-gold gate retains 14,158, 13,622, and 13,436 runs for those conditions.
  • Injected Search results: 8.4%, 11.9%, and 11.6% of P1, P2, and P3 Search payloads require target injection when live results contain no claim-bearing result.The editor inserts the target into the most relevant result and flags the case.
  • Missing and invalid runs: 237 parse failures and 26 API errors remain in the denominator for the two corrupted Code Executor conditions.Search retains 46,184 of 52,643 judged corrupted-condition runs after the primary delivered-payload gate.
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