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arxiv: 2606.00566 · v1 · pith:RDZEGSI5new · submitted 2026-05-30 · 💻 cs.LG · cs.CL· cs.CR

Same Payload, Different Channel: Measuring Trust Asymmetry in Tool-Using Language Models

Mohammed Sameer Syed , Rozhin Yasaei (University of Arizona) This is my paper

Pith reviewed 2026-06-28 19:17 UTC · model grok-4.3

classification 💻 cs.LG cs.CLcs.CR
keywords LLM safetytool-using agentsadversarial attackstrust asymmetrychannel-dependent vulnerabilityagentic models
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The pith

Agent-native models are more vulnerable to the same malicious payload when it arrives via tool descriptions than user messages.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper measures how language models' response to identical adversarial instructions changes depending on whether the content arrives in a user message, tool description, or tool output. It introduces matched payload pairs that keep the malicious text fixed while varying only the delivery context to isolate channel effects. Across six models and three attack types, agent-native models prove substantially more susceptible via tool descriptions while general-purpose models show the opposite pattern. The asymmetry reverses when the same content is placed in tool outputs, indicating models treat tool metadata as trusted instructions and tool results as ordinary data.

Core claim

Using matched payload pairs that keep the malicious text identical but vary only the context of delivery, agent-native models are substantially more vulnerable when adversarial content arrives via tool descriptions than via user messages, while general-purpose models show the reverse. This asymmetry further inverts when the same content is delivered through tool outputs rather than descriptions, suggesting models implicitly treat tool metadata as trusted instructions and tool results as ordinary data. Mechanistic analysis on Llama 3.3 70B shows the safety-relevant representation is causally present at mid-to-late network depths but non-linearly encoded.

What carries the argument

Safety Asymmetry Score (SAS), which quantifies the shift in susceptibility to adversarial content across user messages, tool metadata, and tool outputs using matched payload pairs that hold malicious text constant.

If this is right

  • Safety evaluations of tool-using agents must separately test user messages, tool descriptions, and tool outputs.
  • Agent-native models require stronger defenses specifically against instructions embedded in tool metadata.
  • General-purpose models remain more exposed to direct adversarial user messages even when tool use is enabled.
  • Linear probes are insufficient to detect safety-relevant representations because the encoding is non-linear.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training data for agent models may over-weight tool channels as authoritative sources.
  • Developers could apply channel-specific fine-tuning or filtering to reduce the observed trust asymmetry.
  • Standard red-teaming that focuses only on user prompts will systematically underestimate risks for agent-native systems.

Load-bearing premise

Matched payload pairs with identical malicious text but varied delivery context isolate the effect of the channel without other confounding differences in how the models parse or attend to the inputs.

What would settle it

Re-running the evaluation on the same models with payload pairs that introduce small lexical variations while preserving the malicious intent would falsify the claim if the asymmetry disappears or reverses.

Figures

Figures reproduced from arXiv: 2606.00566 by Mohammed Sameer Syed, Rozhin Yasaei (University of Arizona).

Figure 1
Figure 1. Figure 1: Linear-probe accuracy (adversarial vs. length [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
read the original abstract

As language models take on agentic roles that span calling external APIs, reading tool outputs, and acting on instructions embedded in third-party content, their attack surface expands well beyond what users type. Whether a model treats a malicious instruction the same way regardless of where it arrives has not been systematically studied. We introduce the Safety Asymmetry Score (SAS), which measures how much a model's susceptibility to adversarial content shifts depending on whether that content arrives in the user message, tool metadata, or tool output, using matched payload pairs that keep the malicious text identical and vary only the context of delivery. Evaluated across 6 production LLMs and three attack families, we find a consistent and informative asymmetry: agent-native models are substantially more vulnerable when adversarial content arrives via tool descriptions than via user messages, while general-purpose models show the reverse. This asymmetry further inverts when the same content is delivered through tool outputs rather than descriptions, suggesting models implicitly treat tool metadata as trusted instructions and tool results as ordinary data. A mechanistic study on Llama 3.3 70B reveals that the safety-relevant representation is causally present at mid-to-late network depths but non-linearly encoded, explaining why linear probes fail to detect it. These findings expose a systematic, channel-dependent blind spot in how current tool-using models handle adversarial content.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces the Safety Asymmetry Score (SAS) to measure how a model's susceptibility to adversarial content changes depending on the delivery channel (user message, tool metadata, or tool output), using matched payload pairs that keep the malicious text identical. Evaluated on 6 production LLMs and three attack families, the key finding is that agent-native models are more vulnerable to adversarial content in tool descriptions than user messages, while general-purpose models show the opposite, with the asymmetry inverting for tool outputs. A mechanistic study on Llama 3.3 70B shows safety representations are causally present at mid-to-late depths but non-linearly encoded.

Significance. If the channel effects are isolated from format confounds, this would be a significant contribution to understanding safety in tool-augmented LLMs, with implications for agentic systems. The matched-payload design is a strength for controlling content, and the mechanistic probe adds value by explaining probe failures. The work offers falsifiable, channel-specific predictions.

major comments (2)
  1. Abstract: The SAS definition relies on matched payload pairs that 'vary only the context of delivery,' but the manuscript does not report controls holding input format and structure constant (e.g., JSON schemas vs. free text) while varying only the semantic channel label. This is load-bearing for interpreting the reported asymmetries as trust-related rather than artifacts of tokenization or attention differences.
  2. Mechanistic study on Llama 3.3 70B: The claim that the safety-relevant representation is 'non-linearly encoded' rests on linear probes failing to detect it, but without details on probe architecture, training procedure, or the specific causal intervention used, it is unclear whether non-linearity is the primary explanation or if other factors are involved.
minor comments (1)
  1. Abstract: The description of headline findings supplies no statistical details, error bars, exact SAS formula, or data exclusion rules, which would strengthen assessment of the asymmetry claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the constructive feedback on our work. We address the major comments point by point below, indicating the revisions we plan to make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [—] Abstract: The SAS definition relies on matched payload pairs that 'vary only the context of delivery,' but the manuscript does not report controls holding input format and structure constant (e.g., JSON schemas vs. free text) while varying only the semantic channel label. This is load-bearing for interpreting the reported asymmetries as trust-related rather than artifacts of tokenization or attention differences.

    Authors: We agree that explicit controls for input format would strengthen the claim that the observed asymmetries reflect differences in trust across channels rather than format or tokenization artifacts. In the revised manuscript, we will include additional experiments that hold the input structure constant (e.g., by formatting all delivery channels using the same JSON schema or free-text template) while varying only the semantic channel label. This will allow us to better isolate the effect of the delivery context. revision: yes

  2. Referee: [—] Mechanistic study on Llama 3.3 70B: The claim that the safety-relevant representation is 'non-linearly encoded' rests on linear probes failing to detect it, but without details on probe architecture, training procedure, or the specific causal intervention used, it is unclear whether non-linearity is the primary explanation or if other factors are involved.

    Authors: We acknowledge the need for greater transparency in the mechanistic analysis. The revised manuscript will provide complete details on the linear probe architecture (a single hidden layer MLP), the training procedure (including optimization settings, data splits, and regularization), and the causal intervention (activation patching at specific layers). These additions will clarify that the probe failures are consistent with non-linear encoding of the safety representation. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical measurement via matched payloads

full rationale

The paper introduces SAS as a direct empirical metric computed from model outputs on matched payload pairs that hold malicious text fixed while varying delivery context. No equations, fitted parameters, or derivations are presented that reduce the reported asymmetries to inputs by construction. The central claims rest on observed differences in model behavior across channels rather than any self-referential definition, self-citation chain, or renamed known result. This is a standard empirical evaluation with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the validity of the matched-pair design and the assumption that channel context is the sole manipulated variable; the ledger is populated from the abstract alone.

axioms (1)
  • domain assumption Production LLMs respond to adversarial content in a measurable and consistent enough manner to allow channel comparisons.
    The evaluation across six models presupposes stable behavioral differences attributable to input channel.
invented entities (1)
  • Safety Asymmetry Score (SAS) no independent evidence
    purpose: Quantifies susceptibility shift across delivery channels using matched payloads.
    Newly defined metric introduced to capture the reported asymmetry; no external validation or independent evidence supplied in the abstract.

pith-pipeline@v0.9.1-grok · 5776 in / 1326 out tokens · 28834 ms · 2026-06-28T19:17:29.197151+00:00 · methodology

discussion (0)

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