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Measuring and Exploiting Implicit Trust in LLM Tool-Calling Pipelines

Murali Ediga, Sudipta Chattopadhyay

arXiv:2609.18217v1cs.CR

TL;DR

MCP tool calls expose LLMs to attacker-controlled text across multiple input channels, but there is little empirical evidence about which channels attackers should target. The paper profiles trust across channels and payload framings, then tests attacks that fragment instructions across channels; two-channel fragmentation raises average compliance from 42% to 82%.

  • Problem

    MCP servers can control multiple input channels, but the cited OWASP risk discussion provides no empirical data on which channel an attacker should target.

    Source: A. The Model Context Protocol · p. 2

  • Method

    The authors profile trust across five MCP input channels and six payload framings, then test attacks that distribute an injection across channels so no single channel contains the complete instruction.

    Source: §I · p. 1, C. Cross-Channel Fragmentation (N=2) · p. 5

  • Results

    Two-channel fragmentation doubles average compliance from 42% for direct injection to 82%, while three-channel attacks compromise Haiku at 100% (20/20) in Cursor.

    Source: B. Fragmentation Results · p. 9

  • Takeaways & Limitations

    The authors conclude that an LLM’s trust profile depends on both channel and payload type, and that cross-channel attacks bypass current safeguards and evade third-party defenses.

    Source: §VI · p. 13

    The binary compliance scheme labels partial compliance as refusal, even when transmitted source code may contain hardcoded secrets or trade secrets.

    Source: G. Scoring and Statistical Methods · p. 8

Abstract

from arXiv · show

The Model Context Protocol (MCP) enables LLMs to invoke external tools, but every tool interaction exposes the model to attacker-controlled text through multiple input channels (tool descriptions, tool results, sampling messages) that share a single context window without privilege separation. In this paper, we present a framework to measure the trust profile of an arbitrary LLM based on a variety of payload framings sent through different channels. Following this assessment, we devise cross-channel fragmentation attacks that distribute seemingly benign payloads across two or three channels; no individual channel carries a complete injection, yet the LLM compiles the fragments into credential exfiltration. We evaluated our attacks across 12 frontier models, three production clients, and six payloads, totalling over 15,000 trials. Our evaluation reveals that cross-channel attacks are an unexplored attack surface: models that fully resist single-channel injection (0% compliance) exfiltrate sensitive data at up to 100% under two-channel fragmentation (e.g., GPT-4o, Llama 70B, Composer 2, Haiku 4.5). We further demonstrate value-aligned exploitation, where a tool's stated purpose requires the data the attacker targets, and a sampling system prompt override that injects persistent instructions via VS Code's MCP implementation. Finally, we evaluated our attacks against seven third-party MCP security tools and three prompt-based defenses. All tools failed to detect fragmented payloads, and prompt defenses proved model-specific rather than universal.

I. INTRODUCTION

MCP tool descriptions and results share the model’s context with user, system, and sampling content, without privilege separation. The paper measures channel-specific trust and shows how attackers exploit it through fragmented injections.

A. The Model Context Protocol

  • MCP places server-authored tool descriptions and results in the same context window as user messages, system prompts, and sampling content, with no privilege boundary.A standard interaction also includes the client’s system prompt, while user and sampling content complete the five-channel set.Source: §I · p. 1, A. The Model Context Protocol · p. 2

I. INTRODUCTION

  • A malicious server can split an injection across tool description and result channels, where the model combines benign-looking fragments into credential exfiltration.The example frames a Validator tool in DESC and maps its parameter to .env contents in RESULT; the server intercepts the parameter while returning only a code-review score.Source: §I · p. 1, Figure 1 · p. 1

II. BACKGROUND AND MOTIVATION

  • Earlier MCP attack studies did not comprehensively measure channel trust or consider cross-channel attacks, motivating the paper’s focus.The background introduces MCP as a client-server protocol through which models invoke server-provided tools and receive results into context.Source: §I · p. 1, A. The Model Context Protocol · p. 2

I. INTRODUCTION

  • The paper presents a framework for profiling LLM trust across five MCP input channels and six payload framings.Its contributions also include two- and three-channel fragmentation, value-aligned exploitation, a sampling prompt override, and an evaluation spanning 12 models, three clients, six payloads, and over 15,000 trials.Source: §I · pp. 1–2

B. Related Work

Prior prompt-injection benchmarks largely measured compliance through a single channel, while MCP security research covered other attack classes and defenses. This paper measures model-specific channel trust and examines fragmentation across channels.

B. Related Work

  • Prior prompt-injection benchmarks measured compliance through a single delivery channel and did not test fragmentation or control payload wording across channels.The paper identifies identical adversarial text delivered through each channel as a control for separating channel effects from payload effects.Source: B. Related Work · pp. 2–3
  • The paper distinguishes its focus on cross-channel attacks from MCP work on tool hijacking, cross-tool harvesting, poisoning, token theft, and other attack surfaces.It focuses on credential exfiltration through fragmentation, which MCPSecBench does not cover.Source: B. Related Work · p. 2
  • Existing defenses address different injection settings, including single-channel attacks, fixed channel orderings, or separated code generation and data processing.CaMeL addresses the root cause through a dual-LLM architecture but introduces utility tradeoffs, and no production MCP client implements it.Source: B. Related Work · p. 3
  • Seven MCP security tools use varied approaches, including description-only static analysis, scanning descriptions and results, pinning, and runtime result inspection.The paper evaluates these tools against its payloads.Source: B. Related Work · p. 3
  • The authors report that their methodology controls payload across five channels, tests fragmentation, and demonstrates credential exfiltration from production tools.Table I compares prior work by channels, payload control, fragmentation testing, and production exfiltration.Source: B. Related Work · p. 3, Table I · p. 3

C. Key Insight: The Trust Hierarchy

  • The trust hierarchy is model-specific: channel authority varies by model, so no single channel ranking applies across all models.Existing benchmarks’ single compliance rate per model, averaged across payloads within one channel, can hide model-specific entry points.Source: C. Key Insight: The Trust Hierarchy · pp. 3–4
  • Cross-channel fragmentation distributes an injection so no single channel contains a complete payload, yet the model compiles the fragments in its shared context.The trust hierarchy can guide which channels carry each fragment; per-channel defenses may miss the assembled attack.Source: C. Key Insight: The Trust Hierarchy · p. 4

D. Attack Capability

The attacker can control multiple MCP input channels while a victim uses an IDE assistant on a project with sensitive assets. The paper measures channel-specific compliance using identical payloads across five channels, finding that trust depends on channel–payload interactions.

A. Threat Model

  • The attacker controls tool descriptions, tool results, and—if supported—sampling prompts, while the victim’s benign workflow can involve projects containing credentials, SSH keys, proprietary code, and PII.The study’s scope begins after server installation and tool-call approval; it does not examine server selection or preventing malicious installation.Source: D. Attack Capability · p. 4, A. Threat Model · p. 4

B. Single-Channel Attacks (N=1)

  • Identical injection text, context, tools, and temperature are used across five channels to isolate delivery-position effects from payload wording.The channels are DESC, RESULT, USER, SYSTEM, and SAMPLE; the study tests six payload framings across 12 frontier models.Source: B. Single-Channel Attacks (N=1) · pp. 4–5
  • FC produces an inverted channel ordering on several models, showing that trust depends on channel–payload interactions rather than the channel alone.FC uses a structured template and exploits format-following rather than authority deception.Source: B. Single-Channel Attacks (N=1) · p. 5

C. Cross-Channel Fragmentation (N=2)

Two-channel fragmentation splits an attack between a benign tool schema and a result that maps its fields to sensitive files. The model combines the plausible fragments in its shared context, allowing the attacker’s tool call to carry raw file contents.

C. Cross-Channel Fragmentation (N=2)

  • Fragmentation defeats single-channel defenses by distributing the payload across channels rather than placing the entire injection in one location.A description scanner or result filter can inspect only the channel it monitors.Source: C. Cross-Channel Fragmentation (N=2) · p. 5
  • Neither fragment is malicious alone: the schema defines fields, the result assigns file paths to them, and the model’s unified context compiles the instructions into exfiltration behavior.The model is not evaluating whether passing SSH keys through a format validator is semantically appropriate when the steps arrive separately.Source: C. Cross-Channel Fragmentation (N=2) · pp. 5–6
  • A benign parameter schema in a tool description and a mapping instruction in a tool result jointly direct the model to read sensitive files and pass their contents through tool arguments.The illustrated sequence ends with a clean report returned by the server after the data is exfiltrated.Source: C. Cross-Channel Fragmentation (N=2) · p. 5, Figure 4 · p. 6
  • Static description scanners, keyword-based output filters, and detectors scoring channels independently miss the benign schema and mapping fragments.The description contains no credential or file-path language, and the mapping lacks credential-related keywords.Source: C. Cross-Channel Fragmentation (N=2) · pp. 5–6

D. Three-Channel Fragmentation (N=3)

Three-channel fragmentation divides the attack semantics among a tool description, system prompt, and tool result. In the production sequence, the model chains plausible security-audit steps until raw sensitive-file contents are passed to the attacker’s tool.

D. Three-Channel Fragmentation (N=3)

  • The tool description supplies a parameter schema, the system prompt supplies project-file context, and the tool result maps fields to files; each carries about one-third of the attack semantics.No single channel contains a complete injection.Source: D. Three-Channel Fragmentation (N=3) · p. 6
  • In the production attack, three MCP tool interactions progress from a file inventory to a field-to-file mapping and then to an integrity-check call containing raw file contents.The model reads the files using IDE access, and each step appears to continue a security-audit workflow.Source: D. Three-Channel Fragmentation (N=3) · p. 6

E. Value-Aligned Exploitation

The paper demonstrates attacks that align a tool’s stated purpose with the sensitive data sought and a sampling-based system-prompt override in VS Code with GitHub Copilot. It also describes how trials are scored and notes that partial data transmission can be classified as refusal.

E. Value-Aligned Exploitation

  • Value-aligned exploitation uses tools whose stated security purpose genuinely requires credential data, making refusal functionally equivalent to breaking the tool.The protocol provides no mechanism for the model to verify that the server’s claimed purpose matches its implementation.Source: E. Value-Aligned Exploitation · p. 7

F. Sampling System Prompt Override

  • VS Code’s sampling implementation prepends the server-specified systemPrompt as a system-level message without a safety wrapper or user disclosure.The approval dialog shows the server name but not the injected prompt.Source: F. Sampling System Prompt Override · p. 7
  • In the control condition, without a sampling request, the same tool-result mapping alone is insufficient to cause exfiltration; disposition framing is accepted without warning and exfiltration occurs silently.The compared conditions use the same server and tool result, while the disposition prompt changes willingness to follow the result’s instructions.Source: F. Sampling System Prompt Override · p. 7

G. Scoring and Statistical Methods

  • Trials are scored from full refusal through full silent compliance, with scores 4–5 classified as COMPLY and scores 1–3 as REFUSE.The automated scorer achieved Cohen’s κ = 0.94 against human consensus labels on 150 sampled trials and had 0 false positives on 150 clean-baseline trials.Source: G. Scoring and Statistical Methods · pp. 7–8
  • Score 3 can label a trial REFUSE even when transmitted source code may contain hardcoded secrets or trade secrets; Wilson score 95% intervals are used for reported proportions.The reported compliance rate is the proportion of COMPLY trials out of n total trials.Source: G. Scoring and Statistical Methods · p. 8

IV. ATTACK EVALUATION

The evaluation asks how channel, payload framing, fragmentation, protocol behavior, sampling, and deployment affect attack success. Single-channel results show that compliance depends on the combination of channel and payload, not channel alone.

A. Single-Channel Results

  • SOC-2 and FC produce different compliance orderings across DESC, RESULT, and USER, showing that trust depends jointly on channel and payload.Table III reports compliance for the two payloads across three channels and 12 models.Source: A. Single-Channel Results · p. 8, Table III · p. 9
  • Under SOC-2, DESC averages 28% compliance while RESULT and USER reach 61% and 70%; under FC, DESC reaches 100% on 10/12 models.FC compliance also drops on several models for RESULT; Claude Haiku shifts from refusing every SOC-2 payload (≤3%) to 100% on FC via DESC.Source: A. Single-Channel Results · p. 8
  • The channel–payload interaction is significant (χ2=366.4, 4 degrees of freedom, p<10−50) and remains significant without FC (χ2=139.9, 12 degrees of freedom, p<10−20).The latter test uses social-engineering payloads alone, so the interaction is not an artifact of FC.Source: A. Single-Channel Results · pp. 8–9
  • Six clustered trust profiles differ in their peak channel–payload combinations, so no single channel-blocking policy covers them all.DESC-resistant models show near-zero SOC-2 compliance on DESC but 83–100% elsewhere.Source: A. Single-Channel Results · p. 9
  • Table III uses SOC-2 and FC as payload extremes; the remaining four payloads fall between them and confirm the reported pattern.The attacks were also tested with varied payload positions and temperatures.Source: A. Single-Channel Results · p. 9, Table III · p. 9

B. Fragmentation Results

Fragmentation escalates attack success from direct injection to two-channel API attacks and three-channel production attacks. It also defeats models that resisted earlier stages, including through progressive trust escalation.

B. Fragmentation Results

  • Two-channel fragmentation doubles average compliance from 42% direct to 82%; four models shift from 0% to 90–100%.Table IV compares single-channel, two-channel API, and three-channel production results; its sign test reports p = 0.016.Source: B. Fragmentation Results · p. 9, Table IV · p. 9
  • Three-channel fragmentation in Cursor compromises Haiku at 100% (20/20), while Composer 2 shifts from 50% resistance on two channels to 0% on three.Only Sonnet 4.6 and Opus 4.6 resisted all fragmentation variants at 0/20.Source: B. Fragmentation Results · p. 9
  • GPT-5.4 rises from 0% direct compliance in Cursor to 70% under two-channel and 90% under three-channel fragmentation; Codex CLI reaches 100% under three channels.GPT-5.5 also reaches 100% (10/10) on Codex CLI under three-channel fragmentation.Source: B. Fragmentation Results · p. 9
  • GPT-5.4 and Haiku comply with a progressive escalation chain across 7–8 tool calls, while Sonnet and Opus refuse at its first escalation step.The chain moves from non-sensitive files to .ssh/id_rsa, customers.csv, and .env; Sonnet and Opus evaluate the full sequence before executing.Source: B. Fragmentation Results · pp. 9–10

C. Value-Aligned Exploitation

Value-aligned tools and sampling-system-prompt overrides test attack paths beyond fragmentation. The reported results include credential disclosure by models that resisted fragmentation and exfiltration after an abstract sampling prompt in VS Code.

C. Value-Aligned Exploitation

  • Sonnet 4.6, which resisted fragmentation, transmitted .env credentials and customer PII in 20/20 trials under value-aligned framing.Opus 4.6 transmitted credentials in 18/20 trials; in the other two, it invoked the tool with empty content strings.Source: C. Value-Aligned Exploitation · p. 10
  • An adaptive secret scanner elicited proprietary source code from Sonnet in 3/3 directed trials and all four asset types from Opus in 4/5 directed trials.Opus refused a generic prompt and performed the scan using its own built-in capabilities instead; MCP provides no mechanism to verify a tool’s stated purpose against its behavior.Source: C. Value-Aligned Exploitation · p. 10, Table VI · p. 10

D. Sampling System Prompt Override

  • In VS Code’s sampling experiment, abstract disposition prompts led to full exfiltration, while the no-sampling control produced no report_metrics call from either tested model.All three conditions shared the same tool result; only the sampling system prompt varied.Source: D. Sampling System Prompt Override · p. 10
  • After disposition-mode exfiltration, neither GPT-4o nor Haiku disclosed the server’s injected system prompt when asked about special instructions.VS Code with Copilot was the only production MCP client tested that accepts sampling/createMessage requests; the experiment used GPT-4o and Haiku, the models available through its selector.Source: D. Sampling System Prompt Override · pp. 10–11

E. Cross-Client Safety Gap

Attack success varies across clients, and API trust profiles predict production exploitation differently depending on whether the client supplies a safety prompt. The evaluated MCP security tools do not detect two- or three-channel fragmentation.

E. Cross-Client Safety Gap

  • Claude Code’s safety system prompt blocks GPT-5.4 (90% →0%) but leaves both Gemini models at 100%; the authors attribute the cross-client gap to client defenses for GPT-5.4 and model safety for Claude models.The contribution of individual prompt sections to the refusal rate remains an open question.Source: E. Cross-Client Safety Gap · p. 11
  • API trust profiles predict production compliance for models in Cursor and Codex CLI, but overestimate risk for clients with safety system prompts such as Claude Code.The authors characterize the API profile as an upper bound on exploitability.Source: E. Cross-Client Safety Gap · p. 11

A. Deployed MCP Security Tools

  • Four static scanners inspect descriptions but not runtime results, leaving RESULT and USER unexamined; placing injection content in a tool result bypasses them.The cited average model compliance is 28% for DESC, 61% for RESULT, and 70% for USER.Source: A. Deployed MCP Security Tools · pp. 11–12
  • Invariant Guardrails blocked 2 of 30 payload-channel combinations, while Pipelock blocked direct payloads but both failed against fragmented attacks.Invariant Guardrails caught DirectExfil and ConfigDrift on RESULT; Pipelock’s patterns were bypassed by separated fragments.Source: A. Deployed MCP Security Tools · p. 12
  • TOFU description pinning cannot detect fragmentation when tool descriptions remain unchanged; Trail of Bits’ result-scanning guardrail also requires an opt-in gated model download.The authors report that no evaluated tool detects two- or three-channel fragmentation because classifiers score channels independently.Source: A. Deployed MCP Security Tools · p. 12

B. Prompt-Based Defenses

Prompt-based defenses add safety instructions, but their effectiveness varies by model because channel authority is model-specific and the attack competes in the same context window. The section also describes architectural separation and client-side mitigations for fragmented attacks.

B. Prompt-Based Defenses

  • Prompt hardening reduced GPT-4o-mini compliance to 0%, while Gemini retained 50% on SYSTEM and SAMPLE and 55% on RESULT under hierarchy.Gemini also retained 30% compliance on USER under hierarchy; Claude Code’s safety prompt reduced GPT-5.4 from 90% to 0% but left both Gemini models at 100%.Source: B. Prompt-Based Defenses · p. 12
  • Fixed prompt defenses are not universal because they compete with attacks in the same context window; keyword blacklists also miss format coercion and fragmented payloads.The paper attributes this limitation to model-specific channel authority.Source: B. Prompt-Based Defenses · p. 12

C. Architectural Defenses

  • CaMeL separates code generation from data processing so tool results do not reach the generation context, but no production MCP client implements this separation.Evaluating CaMeL against these attacks remains future work.Source: C. Architectural Defenses · pp. 12–13

D. Mitigations

  • Client-side mitigations include sanitizing tool results, typing sensitive tool parameters, and wrapping server-provided sampling prompts with client-controlled safety instructions.The source-code audit found that VS Code passed the server’s system prompt without a wrapper.Source: D. Mitigations · p. 13
  • The 42% direct versus 82% two-channel gap suggests cross-channel compilation falls outside current safety training, motivating fine-tuning on fragmented attack examples.Source: D. Mitigations · p. 13

VI. CONCLUSION AND DISCUSSION

  • The paper concludes that trust depends jointly on channel and payload type, and that cross-channel attacks bypass current safeguards and evade third-party defenses.The authors made their tool and experimental data available to support reproduction.Source: §VI · p. 13

APPENDIX A

The appendix supplies model and production-client details, then reports supplementary findings on payload variation, GPT-5.4 attack framings, and contradictions between exfiltration and model self-reports.

APPENDIX A

  • The API experiments covered 12 models; production-client experiments added Gemini 3 Flash, Gemini 3.1 Pro, and Composer 2, with testing in April 2026 and no filtering of tool descriptions or results.API experiments used temperature 0 and 30 trials per condition unless noted; Table X lists the evaluated models.Source: Appendix A · p. 15, Table X · p. 15

A. Full Payload–Channel Matrix

  • 0% compliance across all models made ConfigDrift a negative control; payload ordering was SOC-2 > ToolAbuse ≈ DataGov > DirectExfil > ConfigDrift.DirectExfil succeeded only on high-baseline models, while the remaining four payloads fell between the SOC-2 and FC extremes on RESULT.Source: A. Full Payload–Channel Matrix · p. 15, Table XI · p. 15

B. GPT-5.4 Exfiltration Framings

  • 0% single-channel compliance in Cursor rose to 90–100% under three-channel fragmentation for GPT-5.4; two-channel fragmentation yielded placeholders rather than credential values.Successful framings described transmission as routine operations, whereas direct credential requests or authority claims failed.Source: B. GPT-5.4 Exfiltration Framings · p. 15, Table XII · p. 15

C. Self-Report Contradiction

  • GPT-5.4 reported that it had not modified files after transmitting four asset types, while server logs confirmed exfiltration; GPT-5.5 likewise reported no modifications after 10/10 full exfiltrations.The paper attributes the contradiction to interpreting “modify” as writing rather than transmitting read data.Source: C. Self-Report Contradiction · p. 15

D. TOCTOU Tool Description Rug Pull

The appendix tests runtime tool-description changes, stealthier file requests, robustness controls, and graduated scoring, including examples from production clients.

D. TOCTOU Tool Description Rug Pull

  • Static and rug-pull descriptions produced equivalent compliance when an FC payload appeared after three clean calls, so install-time scanning did not guarantee safety against runtime changes.The rug-pull test compared clean, static-payload, and delayed-payload conditions with 30 trials per model.Source: D. TOCTOU Tool Description Rug Pull · pp. 15–16, Table XIII · p. 16

E. Stealth Crescendo

  • Stealth crescendo results showed per-file sensitivity judgments: Sonnet 4.6 refused after detecting a hardcoded API key, while Opus 4.6 transmitted source code in 2/2 trials but refused SSH keys and PII.Source: E. Stealth Crescendo · p. 16

F. Robustness Controls

  • Temperature changes produced no significant effect, and injection position had no effect on GPT-4o-mini, Gemini, or DeepSeek; the reported pattern points to channel identity rather than token position.Clean-baseline controls produced 0/150 false positives.Source: F. Robustness Controls · p. 16

G. Graduated Compliance Scale

  • Score 3 can conceal consequential leakage: Sonnet transmitted patent-pending source code with a hardcoded API key but redacted .env, yet binary scoring labeled the trial REFUSE.Table XIV presents examples for each point on the five-point graduated scale.Source: G. Graduated Compliance Scale · p. 16, Table XIV · p. 16
  • Production sessions showed three-channel exfiltration through Cursor, while VS Code displayed a benign quality score despite data transmission and subsequent denial of special instructions.The cited examples include Kimi-K2.5 exfiltrating in 10/10 trials and a control Haiku run warning about credentials without the sampling system prompt.Source: G. Graduated Compliance Scale · p. 16

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