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What You Approve Is What Executes: Consent Integrity for Black-Box LLM Agents

Xiaoqi Weng

arXiv:2606.02668v1cs.CRcs.HC

TL;DR

Agent-authored approval summaries are forgeable, leaving users to approve the agent’s narration rather than the action itself. The paper defines analyzer-relative Consent Integrity and prototypes a boundary-based mediator; on an independent trusted-tool-abuse corpus, it silently passes 10.0% of cases.

  • Problem

    Current agent approval dialogs trust agent-written summaries, which LITL shows can misrepresent the action that executes.

    Source: §I · p. 1, §V · p. 3

  • Method

    A mediator observes and decodes the real action at the boundary, renders approval independently of agent narration, and binds approval to execution under analyzer-relative Consent Integrity.

    Source: §I · p. 2, §VI · p. 4, §IX · p. 5

  • Results

    10.0% of trusted-tool abuses pass on the independent GTFOBins corpus, while the prototype flags ninety percent of third-party trusted-tool abuses.

    Source: B. Threats to Validity · p. 6, §XI · p. 6

  • Takeaways & Limitations

    The paper presents Consent Integrity as a property and mechanism, while acknowledging that the prototype does not reach its target of zero silent approval bypasses.

    Source: §XI · p. 6

    Total mediation is assumed rather than enforced; an unmediated execution path would bypass the hash-binding check entirely.

    Source: §IX · p. 5, B. Threats to Validity · p. 6

Abstract

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Coding agents gate consequential actions behind a human-in-the-loop approval dialog, but the dialog is narrated by the agent itself: the human approves a summary the agent writes. The Lies-in-the-Loop (LITL) attack shows that summary is forgeable, so a compromised agent can show a benign description while a different action runs. This paper names the missing property, Consent Integrity, by importing What You See Is What You Sign (WYSIWYS) and the trusted-path property into the agent approval channel: the action shown to the human must be rendered by a trusted mediator from the real action at the boundary, not the agent's narration, over a path the agent cannot spoof, and bound to the exact action that executes. Two twists distinguish it from classical WYSIWYS: the renderer is the adversary, and the boundary ground truth is a low-level event that must be decoded without trusting the agent. Since no decoder is complete, the realizable target is analyzer-relative: whatever the analyzer cannot classify is surfaced as uninspectable rather than silently approved. A prototype implements the analyzer, renderer, and bind-to-execution; total mediation and the trusted path are specified but assumed, not implemented. On GTFOBins, an independent corpus of 1330 trusted-tool abuses, the prototype silently passes 10.0% (every instance through a trusted tool); on tldr, 28,798 normal-usage commands, it marks 87.0% uninspectable. These two independent measurements bracket the design's central tension: the trust list that bounds silent passes is the same one that drives over-prompting, and a boundary-only mediator can move along that frontier but not escape it. The contribution is the property, the mechanism, and an honest position on that frontier, not a solved defense.

I. INTRODUCTION

Agent-written approval dialogs can be forged, leaving users to approve a narration rather than the action itself. The paper defines Consent Integrity and proposes a mediator that renders and binds approvals from observed actions.

I. INTRODUCTION

  • Lies-in-the-Loop attacks exploit agent-written dialogs to make a malicious command appear safe for approval.An adversarial agent can pad the payload, hide dangerous commands from view, or provide a misleading summary.Source: §I · p. 1
  • Consent Integrity adapts WYSIWYS and trusted path to require that what a user approves matches what executes.The approval representation must be based on the ground-truth action rather than the agent’s narration.Source: §I · pp. 1–2
  • Black-box agents complicate trusted approval because parts of the model, hosted services, classifiers, and execution harness may not be instrumentable by a third-party defense.The paper identifies this as the dominant real-world setting for agents such as Claude Code, Cline, and Codex CLI.Source: §I · p. 1
  • The agent is the adversarial renderer, while the boundary ground truth is a low-level event that must be decoded into security-relevant meaning.Unlike a wallet transaction, an action such as an execve, network request, or file write may be obfuscated by encoding or indirection.Source: §I · pp. 1–2
  • The proposed mediator renders approval from observed actions and binds it to execution, surfacing uninspectable actions rather than silently approving them.The prototype implements the analyzer, renderer, and binding components, but assumes total mediation and a trusted path.Source: §I · p. 2
  • The prototype flags 90% of trusted-tool abuses, while its default-deny analyzer prompts on the large majority of normal-usage commands.Total mediation and the trusted path are specified and assumed, not implemented.Source: §I · p. 2

II. BACKGROUND AND RELATED WORK

Related work largely secures what agents do with data and tools, while leaving the human-consent interface largely unsecured. Prior prompt-injection work examines compromise and its downstream harms.

II. BACKGROUND AND RELATED WORK

  • Most prior agent defenses govern data flows and tool use, while almost none secures the human-consent interface.The paper positions approval integrity as a distinct concern from what agents do with data and tools.Source: §II · p. 2

A. Prompt Injection and Agent Compromise

B. By-Construction Defenses for the Data and Action Channel

Related defenses address data and tool use, model susceptibility to injection, and hijack benchmarks. These lines study agent behavior rather than whether human approval faithfully represents the action.

B. By-Construction Defenses for the Data and Action Channel

  • By-construction defenses govern autonomous data flow and tool use, but do not render or guarantee the human approval.The paper describes its consent-interface focus as complementary to these defenses.Source: B. By-Construction Defenses for the… · p. 2

C. Model-Level Defenses

  • Model-level defenses train against injection through instruction hierarchy, structured-query finetuning, and preference optimization, but operate inside the model.These approaches reduce but cannot eliminate susceptibility to injection.Source: C. Model-Level Defenses · p. 2

D. Benchmarks

  • AgentDojo, InjecAgent, ToolEmu, and R-Judge measure whether injected instructions redirect agents, not whether approvals faithfully reflect actions.Most use emulated tool environments.Source: D. Benchmarks · p. 2

E. Permission Gates and Agent Authorization

Prior work studies permission decisions and authorization scope, while this paper focuses on whether the approval interface faithfully represents the concrete action. The threat model gives the adversary control of the agent but trusts the mediator and approval path.

E. Permission Gates and Agent Authorization

  • Permission-gate evaluations test classifier decisions over risky actions, whereas this paper targets the integrity of the human-facing approval representation.Overeager-action benchmarks instead study whether agents exceed intended scope on benign tasks [27].Source: E. Permission Gates and Agent… · p. 2

F. Systematizations

  • Recent agentic-AI and MCP systematizations organize attacks and defenses but do not treat the approval channel as a distinct integrity surface [28]–[30].Source: F. Systematizations · p. 2

G. Classical Foundations

  • The construction imports WYSIWYS and trusted path, adapting them to black-box agents and boundary rendering informed by information-flow and sandboxing work –, –.Source: G. Classical Foundations · p. 3

III. THREAT MODEL

  • The adversary controls agent reasoning, tool calls, summaries, and terminal or IDE output, while the mediator, action-boundary interception, and trusted approval path are assumed outside its control.The adversary may attempt actions, misrepresent approvals, and swap an action after approval.Source: §III · p. 3
  • Human comprehension of faithfully rendered actions is out of scope, as are covert-channel harms and kernel-level escapes that break total mediation.The paper treats these as separate from the consent-integrity mechanism or as violations of its mediation assumption.Source: §III · p. 3

IV. THE CONSENT INTEGRITY PROPERTY

Consent Integrity requires approvals to reflect the real action, travel over a trusted path, and remain bound to what executes. Because no decoder can classify every action, the realizable target is analyzer-relative: unclassified actions must not receive a clean approval.

IV. THE CONSENT INTEGRITY PROPERTY

  • Consent Integrity requires truthful rendering from the action itself, faithful presentation of its security-relevant facts, a trusted path, and binding approval to the exact action executed.The ideal definition includes facts such as network egress, credential reads, irreversible destruction, privilege escalation, and writes outside the trusted workspace.Source: §IV · p. 3
  • The ideal decoding condition is unattainable for every action, so the property separates its ideal goal from what a real analyzer can guarantee.Opaque compiled binaries are given as a canonical example of actions that no decoder soundly classifies.Source: §IV · p. 3
  • The prototype evaluates the analyzer and rendering, while total mediation and the trusted path are specified but assumed rather than implemented.Source: Figure 1 · p. 3
  • A silent approval bypass occurs when a security-relevant action executes after approval without its fact being rendered or the action being flagged as uninspectable or high-risk.Source: §IV · p. 3
  • Analyzer-relative Consent Integrity surfaces every fact the analyzer can soundly extract and flags unclassifiable actions as uninspectable or high-risk rather than presenting them as safe.Its realizable target is zero silent approval bypasses.Source: §IV · p. 3

V. WHY EXISTING APPROVALS FAIL

Existing agent approvals trust summaries written by the agent, leaving them vulnerable to forged dialogs and approval-to-execution swaps. CIM instead mediates observed actions, decodes their security-relevant effects, and binds approval to execution, while relying on total mediation and a trusted path.

V. WHY EXISTING APPROVALS FAIL

  • LITL exploits narration-trusting approvals through benign summaries, padding that hides dangerous content, obfuscation, or a post-approval swap from action A to A′.Because the attacker controls the narration, these approvals provide no reliable signal.Source: §V · p. 3

VI. DESIGN: CIM

  • The design assumes total mediation: consequential shell, network, file-write, and tool actions must have no route around the mediator.The prototype evaluates downstream components but does not implement OS-level interception.Source: §VI · pp. 3–4
  • CIM renders approval from the intercepted real action and labels the agent’s narration as untrusted text.Source: §VI · p. 4
  • The analyzer canonicalizes commands, peels common obfuscations, extracts security-relevant facts, and flags actions it cannot inspect as high-risk.For interpreter calls, it analyzes the referenced file; unobserved targets are flagged rather than passed.Source: §VI · p. 4
  • File and build provenance lets CIM reuse verdicts for mediated writes and local builds, while network-fetched artifacts are marked remote-tainted and unknown provenance defaults to uninspectable.The heuristic trusts build tools at the tool level, not across their full dependency closure.Source: §VI · p. 4
  • Each approval binds to the canonical hash of the exact action, so a different action presented for execution under that approval is refused.Source: §VI · p. 4

VII. SECURITY ANALYSIS

Under its threat model and analyzer-relative definition, CIM prevents silent bypasses by observing consequential actions, rendering from those observations, refusing post-approval swaps, and flagging decoding gaps. The guarantee remains bounded by human comprehension, analyzer coverage, total mediation, and trusted-path strength.

VII. SECURITY ANALYSIS

  • Under the threat model and Definition 3, CIM provides analyzer-relative Consent Integrity by construction.Total mediation ensures consequential actions are observed; rendering from that observation defeats summary poisoning and padding, while action binding refuses post-approval swaps.Source: §VII · p. 4
  • Decoder gaps are flagged as uninspectable, so the remaining silent-pass surface comes from trusted actions that the trust list lets proceed without prompts.The trust list is a default-allow island within the default-deny design.Source: §VII · p. 4
  • Faithful rendering does not guarantee user understanding, richer obfuscation can reduce explanation quality, and a sandbox escape or weak trusted path limits the guarantee.A sandbox escape exposing an unmediated path breaks the invariant; the trusted path is weakest in a shared terminal and strongest out-of-band.Source: §VII · p. 4

VIII. IMPLEMENTATION

CIM implements ground-truth analysis, rendering, and bind-to-execution, while total mediation and the trusted path remain assumed. Evaluation on independent attack and normal-usage corpora exposes the trade-off between silent passes and prompting.

VIII. IMPLEMENTATION

  • CIM’s roughly 500-line prototype analyzes actions and provenance, renders ground-truth records, defaults uncertain actions to flagged, and binds approval through canonical-action hashing.It does not implement OS-level total mediation or a trusted path.Source: §VIII · p. 4

IX. EVALUATION

  • 10.0% of 1,330 GTFOBins abuse commands silently pass, while 87.0% of 28,798 tldr commands are marked uninspectable.The same trust list that bounds silent passes on abuse drives over-prompting on normal usage.Source: §IX · p. 5
  • On 22 controlled LITL cases, ground-truth rendering surfaces or flags every attack that the narration-trusting baseline approves.The set is co-designed with the analyzer, so the independent GTFOBins corpus is the external check.Source: §IX · p. 5
  • All 3 post-approval swaps are refused when the executed action’s canonical hash differs from the approved action, assuming total mediation.This is a correctness demonstration, not an empirical finding.Source: §IX · p. 5
  • The analyzer alone adds 0.056 ms mean overhead, but this excludes total mediation and trusted-path rendering.The reported mean is across 4,000 measurements.Source: §IX · p. 5

A. The Central Tension: the Trust List

The trust list sets a trade-off between silent passes and approval fatigue: shrinking it reduces silent passes but increases uninspectable normal commands. The independent-corpus measurements place the prototype on this frontier rather than showing it escapes the trade-off.

A. The Central Tension: the Trust List

  • Shrinking the trust list drives silent passes toward zero but pushes normal-usage uninspectable commands toward 100%.Growing the list relieves fatigue but reintroduces silent passes; a boundary-only static mediator can move along this frontier, not escape it.Source: A. The Central Tension: the Trust List · pp. 5–6
  • For a base64-obfuscated remote-execution payload, the agent displays benign editor-settings narration while CIM renders the true effect.Fig. 2 presents the ground-truth rendering of the payload.Source: A. The Central Tension: the Trust List · p. 6, Figure 2 · p. 6
  • 10.0% of GTFOBins trusted-tool abuses silently pass, while tldr normal usage yields an 87.0% uninspectable rate.The paper attributes the residual GTFOBins passes to trusted tools and identifies the tldr rate as the measured fatigue cost.Source: A. The Central Tension: the Trust List · pp. 5–6

B. Threats to Validity

The controlled-set zero is a co-design artifact, and the independent corpus remains only a stand-in for live agents. Further limits include assumed total mediation and analyzer and provenance boundaries.

B. Threats to Validity

  • The controlled-set zero is unreliable validation because the same author wrote the analyzer and corpus, and an earlier prototype passed common trusted-tool abuses.The paper therefore treats the independent GTFOBins result, rather than the controlled zero, as the figure to trust.Source: B. Threats to Validity · p. 6
  • GTFOBins remains a stand-in for live commercial agents, and the prototype assumes rather than enforces total mediation.The baseline is also described as intentionally weak.Source: B. Threats to Validity · p. 6
  • Referenced-content inspection clears scripts only within bounded operations and otherwise flags them, risking over-prompting and possible under-detection of unenumerated capabilities.The inspection is analyzer-relative.Source: B. Threats to Validity · p. 6
  • Write and build provenance trusts an artifact at the granularity of its build tool rather than its full dependency closure.Source: B. Threats to Validity · p. 6

X. LIMITATIONS AND FUTURE WORK

The paper identifies human comprehension and deeper technical analysis as future work. It concludes that Consent Integrity addresses what an authorization covers and complements defenses that constrain agent data flow.

X. LIMITATIONS AND FUTURE WORK

  • Human comprehension of faithfully rendered actions remains unevaluated and is identified as requiring a human-subjects study.Source: §X · p. 6
  • Future work targets deeper cross-action and interpreter- or syscall-level analysis, richer deobfuscation, full dependency-closure provenance, cross-platform mediation, live-agent evaluation, and trusted-path engineering.The proposed trusted-path work includes shared developer machines and out-of-band confirmation.Source: §X · p. 6

XI. CONCLUSION

  • The realizable guarantee is analyzer-relative, with a target of zero silent approval bypasses; the prototype does not reach that target.Source: §XI · p. 6
  • Consent Integrity complements information-flow defenses: those constrain data leaving an agent process, while this property constrains what action a human authorization covers.Source: §XI · p. 6

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