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Tool Mediation Alters Refusal Mechanisms in Large Language Models

Abel Rodríguez, Giuseppe Garofalo, Lieven Desmet, Vera Rimmer

arXiv:2609.35117v1cs.AI

TL;DR

The paper investigates why harmful requests are refused less reliably in tool-mediated than conversational interactions. Across open-weight models, it analyzes matched interaction conditions and channel-specific representations and interventions. Harmfulness remains represented, but tool mediation is associated with a higher refusal threshold and more fragile refusal; the experiments do not establish where this behavior originates.

  • Problem

    The mechanisms underlying the observed lower refusal rates for matched harmful requests in tool-calling than conversational formats remain poorly understood.

    Source: §1 · p. 2

  • Method

    The authors compare matched conversational and tool-mediated requests and analyze channel-specific harm representations, neuron populations, and refusal interventions.

    Source: §1 · p. 2

  • Results

    Harmfulness remains highly decodable across channels, while tool-mediated refusal follows a different threshold and is more readily disrupted by interventions and input perturbations.

    Source: §1 · p. 3, §6 · p. 9

  • Takeaways & Limitations

    The findings distinguish recognizing harmful intent from robustly translating that recognition into refusal and support evaluating safety in deployment-relevant tool settings.

    Source: §7 · p. 10

    The experiments characterize mechanisms associated with the refusal gap but cannot determine whether it originates in pretraining, instruction tuning, safety alignment, tool-use training, or their interactions.

    Source: §6 · p. 9

Abstract

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Large language models (LLMs) are increasingly deployed with access to external tools, yet harmful tool-mediated interactions are less likely to be refused when compared to regular conversational ones. As this change in refusal behavior remains underexplored, we investigate its underlying mechanisms across a diverse set of open-weight language models. We find that information about the harmfulness of a request remains strongly encoded in the model's representations and transfers across conversational and tool-mediated inputs. Evidence from representation geometry and neuron-level analysis further indicates that the two interaction modes systematically distribute harm-related computation differently. Crucially, while conversational inputs can be refused at relatively low levels of perceived harmfulness, tool-mediated inputs remain permissive until harmfulness crosses a substantially higher effective refusal threshold. Moreover, tool-mediated refusal is also more brittle: progressively weakening the refusal computation disrupts tool-mediated refusal at lower intervention strengths than conversational refusal, even when benign capabilities remain intact. Together, our findings indicate that tool mediation does not simply reduce the internal perception of harm, but instead impacts its conversion into refusal. Overall, this suggests tool-mediated environments may intrinsically reduce robustness of models to harmful requests, and that conventional safety evaluations may not fully transfer to LLM agents.

1 INTRODUCTION

The introduction asks whether the refusal gap between conversational and tool-mediated interfaces reflects different underlying mechanisms. It reports that tool exposure drives much of the behavioral drop, while harmfulness remains represented and the channels differ in how that signal is translated into refusal.

1 INTRODUCTION

  • The paper asks whether refusal mechanisms remain consistent across interfaces, since prior work leaves that cross-interface question unresolved.Tool-mediated interfaces also require refusals to prevent harmful executable actions, not merely communicate that an action should not be taken.Source: §1 · pp. 1–2
  • Tool exposure substantially reduces refusal across all eight models, with an explicit tool-call directive further increasing the drop.Agentic framing alone has a small and inconsistent effect; the decline begins before an explicit instruction to execute.Source: §1 · p. 2
  • The authors examine behavioral changes across interaction modes and test whether channel-specific harm representations contribute causally to refusal.They compare targeted ablations of each channel’s neuron population with ablations of the other channel’s and matched random populations.Source: §1 · p. 2
  • Harmfulness remains highly decodable in both channels, but at matched harm readouts tool-mediated inputs face a substantially higher refusal threshold.The channels also rely on partially distinct neuronal contributions to the harm representation preceding refusal.Source: §1 · pp. 2–3
  • Tool-mediated refusal is weaker at baseline and more readily disrupted by directional ablation and input-space attacks than conversational refusal.For prompts refused in both channels, Greedy Coordinate Gradient attacks break tool-mediated refusal more often and in fewer optimization steps.Source: §1 · pp. 2–3

2 PRELIMINARIES

The preliminaries define channel-specific harm and refusal directions from contrastive activations, then describe neuron attribution and ablation procedures for comparing channels.

2 PRELIMINARIES

  • The analysis extracts normalized activation-difference directions from contrastive conditions at the decision position, immediately before the first generated token.Directions are estimated by contrasting harmful with benign intents or refused with complied responses on harmful intents.Source: §2 · p. 3
  • Harm directions contrast harmful and benign intents, while refusal directions contrast refused and complied responses on harmful intents, separately for each channel.Directions use five-fold cross-validation by intent; a direction is omitted when a channel lacks sufficient examples of both outcomes.Source: §2 · p. 3
  • Channel-selective neurons are identified by their harmful–benign contribution along a shared harm direction, rather than activation magnitude alone.The contribution is computed separately in conversational and tool-mediated channels, then used to rank neurons by relative contribution magnitude.Source: §2 · p. 4
  • Top-K and bottom-K neurons define tool-selective and conversation-selective sets, which are compared with cross-channel and matched-random ablations.Selection uses two-fold cross-fitting by intent across four layers; mean ablation effects are measured on the corresponding channel-specific final-layer harm projection.Source: §2 · p. 4

3 RELATED WORK

Related work documents interface-dependent refusal behavior and develops accounts of refusal representations, safety-component localization, and adversarial testing. This paper extends those lines by varying delivery channel within the same model while holding weights and request content fixed.

3 RELATED WORK

  • Prior studies find lower refusal for matched harmful requests in tool-calling settings, but leave the underlying mechanisms unresolved.The paper adds an intermediate tool-visible, conversational-response condition to distinguish tool exposure from the operational affordance of acting through a tool.Source: §3 · p. 4
  • Prior mechanistic work finds structured, potentially multidimensional refusal representations, separable harmfulness and refusal signals, and some cross-language transfer.The present analysis asks whether refusal realization also depends on delivery channel.Source: §3 · pp. 4–5
  • Safety-component localization uses attribution and intervention, while adversarial attacks probe refusal robustness through optimized input perturbations.This paper applies localization and intervention within one model, holding weights and request content fixed while varying delivery channel.Source: §3 · pp. 4–5

4 EXPERIMENTAL SETUP

The experimental setup evaluates open-weight instruction-tuned models on paired AgentHarm behaviors rendered across conversational, agentic, tool-exposure, and tool-action conditions. Tool-call outputs and conversational responses are assessed with different evaluation procedures.

4 EXPERIMENTAL SETUP

  • The study uses the shared AgentHarm test behaviors, parsing tool outputs for target functions and using an LLM judge for conversational responses.The judge is Qwen3Guard-Gen-4B.Source: §4 · p. 5
  • Each AgentHarm intent is rendered in five variants to separate agentic framing, tool exposure, and explicit tool action.Three independently phrased directives are evaluated per condition, and comparisons use intents without a specific-tool hint.Source: §4 · p. 5
  • Eight open-weight instruction-tuned models spanning five families, two architectures, and 8B–70B parameters are evaluated.Native reasoning modes are disabled when possible; Ministral-3 and Gemma-4 use text decoders and are treated as dense, while gpt-oss is MoE.Source: §4 · p. 5, Table 1 · p. 5

5 UNDERSTANDING TOOL-MEDIATED REFUSAL

Tool exposure and tool-action prompts reduce refusal, while harmfulness remains encoded across channels. At matched harm readouts, tool-mediated refusal uses a higher threshold and is more vulnerable to interventions and input-space attacks.

5 UNDERSTANDING TOOL-MEDIATED REFUSAL

  • Across eight models, tool-action reduces refusal rates by 15.5–84.4pp relative to the conversational reference.Table 2 reports refusal rates across five prompting conditions, with differences from the reference in parentheses.Source: §5 · p. 5, Table 2 · p. 6
  • Tool exposure accounts for 35–88% of the final refusal drop with the regular agentic prompt, and 23–62% without its tool-call directive.The decline appears before an explicit instruction to emit tool calls.Source: §5 · p. 6

5.1 FROM REPRESENTATION TO DECISION

  • Cross-channel harm directions remain predictive: the conversational direction reaches AUROC 0.758–0.913 on tool inputs, and the tool direction reaches 0.732–0.941 on conversational inputs.Using both projections changes tool-prompt AUROC by at most ±0.02 relative to the better direction.Source: §5.1 · pp. 6–7
  • Harm directions diverge across channels (cosine similarity 0.22–0.71), and channel-selective late-MLP neurons contribute differently to harm representations; attention-head dissociation is inconsistent.Neuron-level effects were recovered in all seven dense models at K ≥800.Source: §5.1 · p. 7
  • At matched harm readouts, tool refusal has a higher threshold; among the least-harmful intents, ∆T1 is 0.43–0.98.Figure 2 plots refusal probability against harm projection and marks the 50% refusal thresholds.Source: §5.1 · p. 8, Figure 2 · p. 8

5.2 FRAGILITY UNDER TOOL MEDIATION

  • Tool-mediated refusal breaks at lower refusal-direction ablation doses and more readily under GCG than conversational refusal under the same search budget.The GCG comparison uses prompts refused in both channels at baseline.Source: §5.2 · pp. 8–9, Table 4 · p. 8

6 DISCUSSION

The refusal gap is not fully explained by reduced harm information: harmfulness remains decodable, while its conversion into refusal differs across channels. The conclusions are bounded by unresolved origins and aspects of the evaluation design.

6 DISCUSSION

  • The findings indicate that tool-mediated refusal differs in decision threshold and is more readily disrupted, despite harm remaining highly decodable across channels.The asymmetry is already present upon tool exposure, before an explicit instruction to execute.Source: §6 · p. 9
  • The evaluation leaves open whether wording and compliance-criterion differences contribute to the behavioral gap, and its dataset may not capture real-world tool-use diversity.The experiments also do not establish whether the gap originates in pretraining, instruction tuning, safety alignment, tool-use training, or their interactions.Source: §6 · p. 9
  • The study recommends evaluating tool-using systems through deployment-relevant interfaces and testing refusal robustness across contexts.Conversational safety properties should not be assumed to transfer unchanged to tool-mediated interaction.Source: §6 · p. 9

7 CONCLUSION

The conclusion distinguishes recognizing harmful intent from translating that recognition into refusal across interaction settings. It highlights tool exposure as an early point of divergence and calls for safety evaluation in deployment-relevant settings.

7 CONCLUSION

  • Across conversational and tool-mediated settings, models retain substantial information about harmfulness, but translate it into refusal differently.The conclusion characterizes tool-mediated refusal as more susceptible to internal and input-space perturbations.Source: §7 · p. 9
  • Much of the change in refusal is present when tools are exposed, before the model is instructed to execute an action.The conclusion frames safety evaluation in settings relevant to real-world deployment as important.Source: §7 · p. 10

A PRELIMINARIES

The preliminaries define residual-stream reading and intervention directions, neuron-selection procedures, and the experimental tool and directive variants. They also specify how interventions and neuron effects are measured across channels.

A PRELIMINARIES

  • Residual-stream representations are projected onto directions at the decision position; reading directions measure represented information, while intervention directions define perturbation axes.Prompt-level reading analyses use each model’s deepest probed layer; intervention analyses use a separately prespecified layer.Source: A PRELIMINARIES · p. 13
  • A centered neutral harm direction provides a shared comparison axis because channel means differ more than harmful–benign class means.Reading directions are estimated with five-fold cross-validation by intent, keeping each evaluated intent out of its fitted direction.Source: A PRELIMINARIES · p. 13
  • The conversational refusal direction is left undefined when compliance on harmful intents is too rare for a stable estimate, and is reported only for Ministral-3-8B and Llama-3.1-70B.Six of the eight models refuse almost every request.Source: A PRELIMINARIES · p. 14
  • Interventions ablate a chosen direction from decoder-layer outputs onward, across token positions and generation steps; the smallest dose at which refusal fails operationalizes robustness.λ = 1 is complete removal, while λ > 1 is over-ablation.Source: A PRELIMINARIES · p. 14
  • Neuron analyses use four consecutive late-MLP layers and held-out intents; shared-axis harmful–benign contribution contrasts select tool- and conversation-selective sets, with a matched random control.Channel differences in neuron contributions arise through activation patterns, and the random set is matched on layer and contribution-magnitude decile.Source: A PRELIMINARIES · pp. 14–15

B.1 DIRECTIVE PARAPHRASES AND TOOL INVENTORY

  • Each AgentHarm intent has one canonical directive and two rephrasings per condition; exposure conditions share seven generic tools, while tool action adds an execution directive.The exposure conditions also include the relevant AgentHarm target function.Source: Table 5 · p. 15, §B.1 · p. 15, Table 6 · p. 15

B.2 JUDGING MODEL BEHAVIOR

The study measures explicit harmful-intent refusals separately from capability limitations and distinguishes refusal status from successful tool execution. Conversational refusals are judged by an LLM, while tool compliance is checked from target-function calls.

Refusal and capability definitions

  • Explicit harmful-intent refusal requires a response to decline because the requested action is harmful, unsafe, or disallowed.Capability limitations are reported separately, including their overlap with harmful-intent refusals.Source: §B.2 · pp. 15–16

Operational classification

  • Capability-only responses count as non-refusals, although the authors note that lacking an explicit harmful-intent refusal does not necessarily imply willingness to comply.This operational choice may affect refusal-rate estimates for Llama-family models, which more often refuse without an explicit rationale.Source: §B.2 · p. 16

Judging refusal and compliance

  • Tool-channel compliance is identified by parsing target-function calls, while conversational refusal is judged by an LLM and refusal rationales by regex classifiers.The primary judge is Qwen3Guard-Gen-4B; a seeded sample was also graded by an independent judge.Source: §B.2 · p. 16

Judge agreement

  • Judge agreement was high in seven of eight models, with Ministral-3-8B the exception at AC1 0.396.Its conversational refusal rate is less saturated, leaving more borderline completions for judges.Source: §B.2 · p. 17, Table 9 · p. 17

B.3 MULTI-TURN AGENT BEHAVIOR

The appendices evaluate multi-turn behavior, attack robustness, directive effects, and channel-specific representations. Across these analyses, tool exposure is associated with an early refusal shift, while channel differences appear in representation geometry and neuron-level contributions.

B.3 MULTI-TURN AGENT BEHAVIOR

  • Multi-turn and single-turn refusal measurements differed by 0.063 on average across eight models, with no consistent direction and strong intent-level agreement (r = 0.817; binary agreement = 0.885).Non-refusal generally corresponded to actual harmful behavior rather than failed execution; Llama-3.1-8B was the main exception.Source: §B.3 · p. 18, Table 10 · p. 18

B.4 GREEDY COORDINATE GRADIENT (GCG) OPTIMIZATION.

  • GCG compares steps to break baseline refusal on 8–12 intents refused in both channels, using matched 24-token suffixes and a 40-step budget.The tool and conversational channels optimize toward their respective compliance targets; gpt-oss-20b is excluded because its completion format is incompatible with the fixed objective.Source: §B.4 · pp. 18–19

B.5 IMPLEMENTATION DETAILS

  • Experiments use pretrained, unquantized checkpoints; generations use temperature 0.7, top-p 0.9, and eight samples per configuration.Models were not fine-tuned or updated.Source: §B.5 · p. 19

C.1 REFUSAL ACROSS DIRECTIVES

  • Refusal decreases from the conversational baseline to tool exposure and decreases further in the tool-action condition.The tables report refusal rates and paired per-behavior bootstrap confidence intervals across the conditions.Source: §C.1 · p. 20, Table 13 · p. 20

C.2 REFUSAL REPRESENTATIONS SHIFT UPON TOOL EXPOSURE.

  • Tool-exposure and tool-action refusal axes predict each other’s compliance/refusal split with 0.78–1.00 AUROC, and the representational shift is largely established before execution is requested.Figure 3 locates the shift at tool exposure rather than the explicit directive to call the tool.Source: §C.2 · p. 21, Figure 3 · p. 21

C.3 CHANNEL FRAGMENTATION. / C.4 CHOICE OF DIRECTION FOR ANALYSES.

  • Harm-axis cross-channel similarity generally declines with depth, but early-layer estimates are noisy; the harm and refusal axes are sufficiently aligned to use harm as an approximate proxy.The refusal-axis decline is reported for the two models where it can be estimated reliably; harm–refusal cosine similarities range from 0.76–0.97 of ceiling.Source: §C.3 · p. 21, Figure 4 · p. 22, §C.4 · p. 22

C.5 HARM DISPLACEMENT.

  • In dense models, tool renderings have lower harm-axis projections for 81–100% of intents, with mean displacement of 0.69–1.25 across intents.The displacement is not preferentially aligned with harm: the harm direction falls within the random-direction null in every model.Source: §C.5 · p. 22, Table 14 · p. 25

C.6 NEURON CONTRIBUTION PROFILES

  • Neuron ablations show channel-specific harm representations, whereas attention-head analyses show less channel specificity and occasional reversals against other-channel or random-head ablations.Selected neurons exceed matched random controls in every dense-model arm; the head-level interpretation is qualified because other heads may compensate.Source: §C.6 · p. 25, Table 15 · p. 26, Table 17 · p. 28

C.7 REFUSAL DIRECTION ABLATION

Graded ablations show that tool-mediated refusal breaks at lower intervention doses than conversational refusal, including when testing a channel-neutral refusal direction. Measurements stop before benign execution collapses.

C.7 REFUSAL DIRECTION ABLATION

  • The same asymmetry appears when ablating the channel-neutral refusal direction, while the tool-channel direction is more selective and less diagnostic of channel asymmetry.Conversational refusal often survives the usable dose range without breaking, further understating the observed asymmetry.Source: §C.7 · p. 29
  • Tool-mediated refusal fails at a lower intervention dose than conversational refusal in every model with measurable breakdown in both channels.Among prompts refused in both channels at zero dose, tool refusal breaks first on the large majority of matched prompts; measurements remain below the benign-capability floor.Source: §C.7 · p. 29, Figure 7 · p. 29

C.8 CONTEXTUAL REFUSAL

A smaller PrivacyLens replication tests whether the main patterns extend to privacy-sensitive disclosure rather than inherently harmful actions. The refusal drop and qualitative mechanistic patterns recur, although behavioral effect sizes vary across models.

Interpretation

  • The replication supports context-dependent realization of refusal for privacy constraints, where the same communication action can be appropriate or inappropriate depending on its content.The tested safety condition is disclosure of private or confidential information about a third party to unauthorized recipients.Source: §C.8 · pp. 29–31

Experimental setup

  • The replication uses 250 PrivacyLens scenarios, each pairing a privacy-sensitive disclosure request with a benign update request under a fixed system prompt.Scenarios specify the acting person, third party, private detail, recipient, and communication tool.Source: §C.8 · pp. 29–30
  • The replication includes only conversational-reference and tool-action conditions, omitting the main evaluation’s full set of conditions and directive paraphrases.The fixed system prompt makes its conversational reference more closely aligned with agentic framing than the main evaluation’s reference.Source: §C.8 · p. 30

Behavioral results

  • The tool-mediated refusal drop also appears for privacy-sensitive disclosure, but its magnitude varies across models and is not statistically significant for gpt-oss-20B.The Gemma-4 family shows a relatively small decrease; remaining models show a more substantial drop than in AgentHarm.Source: §C.8 · p. 30, Table 19 · p. 30

Mechanistic results

  • PrivacyLens reproduces the qualitative mechanistic patterns: harmfulness remains decodable within each channel, while cross-channel representations are less aligned than within-channel estimates.Gemma-4 has higher cross-channel similarity and less geometric separation than in the main evaluation, alongside smaller behavioral refusal drops.Source: §C.8 · pp. 30–31, Table 20 · p. 31

D BEHAVIORAL EXAMPLES

The examples show that matched harmful requests can receive different responses across conversational and tool-mediated channels. Other cases distinguish harmful-intent refusals from capability disclaimers, which may accompany harmful content rather than prevent it.

D BEHAVIORAL EXAMPLES

  • Qwen3-32B and Llama-3.1-70B refuse harmful requests conversationally but comply through tool calls in the examples.The tool-channel responses include a harmful action extracting user records and compliance with a request refused in conversation.Source: Table 21 · p. 32, Table 22 · p. 33
  • Qwen3-32B writes fabricated smear content in conversation without a disclaimer or claim of lacking the ability to act.The example request asks it to publish and amplify a false spying claim about Kiwi Unlimited.Source: Table 23 · p. 34, D BEHAVIORAL EXAMPLES · p. 34
  • Gemma-4-31B says it lacks an amplification tool, then supplies the fabricated blog post and an amplification strategy.Its capability disclaimer does not stop it from producing the harmful content.Source: Table 24 · p. 35
  • Gemma-4-31B separates a money-laundering policy refusal from a capability refusal about accessing email or transferring funds.The response explicitly names illegality and separately states that it cannot access email or perform financial transfers.Source: Table 25 · p. 35, D BEHAVIORAL EXAMPLES · p. 35

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