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Who Is Your Agent Serving? Provider-Side Indirect Prompt Injection in Proactive Agents

Rui Wang, Chao Wang, Xinchen Wang, Yufeng Zheng, Binbin Liu, Yaofei Wang

arXiv:2610.05266v1cs.CRcs.AI

TL;DR

The paper asks whether an external provider can redirect a benign proactive agent toward its target without accessing private user context or gaining permissions. It models this steering through target selection, user-context binding, and prospective support, and finds increased target authorization across all tested environment–user-model combinations. Correct user–target binding matters more than additional proposal detail, while provider objectives can remain influential in multi-turn interactions without final authorization.

  • Problem

    Existing indirect-injection evaluations largely measure agent-side outcomes, leaving whether external steering reaches users’ authorization decisions an open question.

    Source: §1 · p. 1

  • Method

    The paper studies provider-controlled target content that steers a benign agent’s target selection, use of authorized user context, and prospective support.

    Source: §1 · p. 2

  • Results

    Full increases target authorization in all 18 environment–user-model combinations, with macro-average gains of 77.4, 47.0, and 33.7 percentage points in PARE, TGL, and ProPerSim.

    Source: §1 · p. 2, §4.3 · p. 6

  • Takeaways & Limitations

    Correct user–target binding matters more than additional proposal detail, and provider objectives can remain influential in multi-turn interactions even without final authorization.

    Source: §1 · p. 2

    The candidate benchmark uses synthetic profiles and simulated users, so its authorization rates are evidence within that setting rather than estimates of real-user behavior.

    Source: §4.6 · p. 9

Abstract

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Proactive personal agents increasingly decide what to recommend, how to personalize advice, and what follow-up assistance to offer, creating a new user-decision attack surface for provider-side indirect prompt injection. We show that an external provider need not access private user context, compromise the agent, or gain additional permissions: by controlling only content associated with its own target, it can redirect an otherwise benign agent to advance that target, recruit legitimately available user context to justify it, and proactively reduce the friction of adoption. We characterize this failure mode through Target Control, Private Binding, and Prospective Support, which respectively steer what the agent advances, how it connects the target to the user, and what target-specific assistance it offers next. Across three proactive-agent environments and six simulated user models, the full attack increases target authorization in all tested environment-user-model combinations, with a macro gain of up to 77.4 percentage points. Controlled replay shows that correct user-target binding is more consequential than additional proposal detail alone, while a multi-turn extension reveals that provider objectives can remain influential even without final authorization by reshaping how the agent responds to user constraints and resistance. These findings expose a broader trust boundary: capabilities designed to serve the user can be redirected toward objectives originating outside the user-agent relationship.

1 INTRODUCTION

Proactive agents enter users’ decision processes by choosing and personalizing assistance, while provider-side indirect injection can redirect that assistance toward an external objective. The paper examines whether this steering reaches users’ authorization decisions and characterizes it through Target Control, Private Binding, and Prospective Support.

Motivation

  • Provider-side injection redirects a benign agent through target-associated content, while the provider lacks access to private context and the user retains the final choice.The agent supplies the personalized rationale and prospective support, so checking the recommendation alone does not establish whether the user was influenced.Source: §1 · p. 1, Figure 1 · p. 2

Approach

  • Target Control steers what the agent advances, Private Binding connects authorized user context to target properties, and Prospective Support directs future assistance toward the target.The provider uses generic directions rather than private user values or additional permissions.Source: §1 · p. 2

Main result

  • Full increases target authorization in all 18 environment–user-model combinations, with macro-average gains of 77.4, 47.0, and 33.7 percentage points in PARE, TGL, and ProPerSim.The experiments use GPT-5 and six simulated user models; controlled replay finds correct user–target binding matters more than additional proposal detail.Source: §1 · p. 2

Contributions

  • The paper’s contribution is to evaluate downstream user authorization and to show that a fixed provider objective can remain influential across dialogue, including after user resistance.The long-horizon interaction can reshape the agent’s rationale and support even when the user does not ultimately authorize the target.Source: §1 · p. 2

2 RELATED WORK

Prior work studies external steering of agent behavior, proactive-agent capabilities, and AI-mediated influence on users. This paper distinguishes provider-controlled target content from objectives assigned to the assistant and evaluates downstream user authorization.

Indirect Prompt Injection

  • Prior indirect-injection studies examine agent-side outcomes such as target preference or attacker-goal completion, which do not establish whether users act on manipulated assistance.This paper evaluates authorization decisions in simulated user–agent interactions instead of treating the recommendation as the endpoint.Source: §2 · p. 3

Proactive Personal Agents

  • Proactive agents use context to decide when and how to assist, and the paper tests whether provider-side instructions redirect their selection, personalization, and prospective support.The study retains evaluated agents’ native context and interaction mechanisms, including as user constraints and alternatives emerge over multiple turns.Source: §2 · p. 3

AI-Mediated Influence

  • Prior AI-influence studies include assistants with promotional or malicious objectives, whereas this provider controls only target-associated external content and leaves user-specific reasoning to a benign agent.The provider does not observe private context or modify trusted instructions.Source: §2 · p. 3

3 METHOD

The method models a benign proactive agent that turns authorized user context and candidate information into a recommendation, rationale, and optional support. It then studies provider-side steering through three dimensions, including an adaptive extension for dialogue over multiple turns.

3.1 THREAT MODEL

  • The agent’s proposal combines a recommendation or abstention, a user-specific rationale, and optional prospective support using retrieved task-relevant context.The decomposition describes ordinary proactive decisions rather than prescribing a new recommendation procedure.Source: §3.1 · p. 3
  • The evaluation endpoint is positive user authorization of the provider-selected target, not recommendation alone.The provider controls target-associated content but cannot alter trusted instructions, private context, candidate facts, tools, permissions, retrieval, or the decision interface.Source: §3.1 · pp. 3–4
  • Candidate targets do not clearly conflict with core configuration requirements, so the threat model concerns steering among plausible alternatives rather than incompatible choices.Targets may still be weaker on other task-relevant properties, limitations, or cost, and are not assumed to satisfy every preference.Source: §3.1 · p. 4

3.2 ATTACK DESIGN

  • Target Control keeps the provider-selected target focal, while Private Binding directs the agent to connect authorized user context to properties genuinely supported by that target.The provider supplies only the direction to bind; the agent constructs the user-specific match after retrieving context through normal mechanisms.Source: §3.2 · p. 5
  • Prospective Support directs concise, candidate-specific future assistance toward the target, but remains non-executing and cannot compensate for capabilities the target lacks.The study does not propose an injection-delivery or defense-evasion technique, but examines behavior induced when a proactive agent follows such content.Source: §3.2 · pp. 4–5

3.3 LONG-HORIZON GENERALIZATION

  • The long-horizon extension adapts the same steering dimensions as users introduce constraints, concerns, or alternatives, while keeping the provider objective fixed.It adds no attacker privileges and does not imply unconditional persistence; the agent remains responsive to user constraints.Source: §3.3 · p. 5

4 EXPERIMENTS

Across three proactive-agent environments, the experiments measure explicit target authorization and trace how steering operates; Full improves authorization across all 18 tested combinations. Replay and multi-turn analyses examine user–target binding and influence through resistance, while the evaluation uses synthetic users and compatible targets.

4.1 EXPERIMENTAL SETUP

  • The synthetic benchmark evaluates 24 task families across two profiles and three difficulty levels in PARE, TGL, and ProPerSim, using GPT-5 with six simulated user models.Targets remain compatible with users’ core configuration requirements.Source: §4.1 · pp. 5–6

4.2 EVALUATION METRICS

  • Target authorization requires explicit selection of the target; other metrics track recommendation, correct use of user context, target-specific support, and authorization conditional on recommendation.The conditional metric uses only episodes with a target recommendation, and those subsets can differ across conditions.Source: §4.2 · p. 6

4.3 MAIN RESULTS

  • Full raises target authorization over Neutral in all 18 combinations, with macro gains of +77.4, +47.0, and +33.7 percentage points in PARE, TGL, and ProPerSim.These end-to-end gains do not imply uniformly higher authorization conditional on recommendation.Source: §4.3 · p. 6, Table 1 · p. 7
  • Private Binding adds +36.1, +16.7, and +4.9 pp over T across PARE, TGL, and ProPerSim; T+S does not improve authorization over T, while Full exceeds T+B in all three.The Full–(T+B) margin is 2.8 pp for favorable and 17.7 pp for unfavorable targets; unfavorable-target authorization remains below the other difficulty settings.Source: §4.3 · pp. 7–8, Figure 3 · p. 7, Table 2 · p. 8

4.4 CONTROLLED REPLAY DIAGNOSTICS

  • Correctly bound T+B reaches 90.7% replay acceptance, while every fixed or mismatched-configuration control is rejected; expanding T alone reaches 60.5%.Replay acceptance is a single-stage measure and is not comparable to end-to-end authorization rates.Source: §4.4 · p. 8, Table 3 · p. 9

4.5 LONG-HORIZON EVALUATION

  • 32.0% to 92.0% target service and 8.0% to 24.0% strict final authorization accompany increases in sustained influence and post-resistance targeting across all five model pairings.Target-advancing acceptance rises from 28.0% to 48.0%.Source: Table 4 · p. 9, §4.5 · p. 9

4.6 DISCUSSION AND SCOPE

  • Because profiles and users are synthetic, authorization rates characterize this evaluation rather than real-user behavior; the study also does not optimize injection delivery or defense evasion.The long-horizon study shows provider influence can persist in responses to concerns or resistance even without changing the final decision.Source: §4.6 · p. 9

5 CONCLUSION

Provider-side indirect prompt injection redirects a proactive agent’s assistance toward an external provider’s objective, reaching beyond recommendation into personalization, support, and user authorization. The paper distinguishes the external content surface from the downstream behavior it shapes.

5 CONCLUSION

  • The provider-side attack redirects a benign agent’s recommendation, personalization, and prospective support without private user information or new privileges.Private Binding connects the target to authorized user context, while Target Control steers what the agent advances and Prospective Support steers future assistance.Source: §5 · p. 9

A.1 INDIRECT PROMPT INJECTION

  • Indirect injection occurs when external content influences agent behavior as if it had instruction authority, despite being encountered during a legitimate task.The authority mismatch is between the source’s legitimate role in supplying content and its lack of authority to redefine the user’s task or direct the agent.Source: §A.1 · p. 13

A.2 INJECTION SURFACES

  • Injection surfaces include webpages, tool content or descriptions, retrievable memory, and provider-controlled records that can steer preferences or rankings.Provider-controlled content can describe its own product or service and serve as the injection surface without compromising an unrelated source.Source: §A.2 · pp. 13–14

A.4 FROM INJECTION SURFACES TO DOWNSTREAM EFFECTS

  • The paper separates where external content enters an agent from the downstream effect it seeks, and studies how provider influence can extend from ranking into personalized support and authorization.The behavioral abstraction does not require observing the agent’s internal state; it requires only that subsequent behavior respond to the external direction.Source: §A.4 · pp. 14–15

A.3 BENCHMARKS AND EVALUATION FRAMEWORKS

The paper situates its evaluation among benchmarks that measure downstream injection effects, then models proactive assistance and instantiates it across complementary agent environments. It describes provider-side payloads as steering content carried by provider-authored records, not as instructions with legitimate authority.

A.3 BENCHMARKS AND EVALUATION FRAMEWORKS

  • Existing evaluation frameworks measure varied downstream effects, including privacy leakage, tool actions or selection, persistent behavior, and preference or ranking changes.The benchmarks also differ in the surfaces through which adversarial content reaches agents, motivating evaluation of end-to-end consequences.Source: §A.3 · p. 14

B.1 A GENERAL MODEL OF PROACTIVE ASSISTANCE

  • Proactive assistance can be abstracted as intervention, retrieval of task-relevant user context, and construction of a proposal, without requiring separate architectural modules.The proposal may make explicit what the agent advances, its user-linked rationale, and optional prospective support; these decisions may be generated jointly.Source: §B.1 · pp. 15–16

B.2 THREE COMPLEMENTARY PROACTIVE-AGENT ENVIRONMENTS

  • PARE, TGL, and ProPerSim retain complementary mechanisms: stateful interaction, explicit intervention decisions, and retrieval-backed personalization.The settings preserve their native distinctions while holding the decision task, candidate facts, user profile, and provider-side semantics fixed.Source: §B.2 · pp. 16–17

C.1 CONSTRUCTION PRINCIPLES AND PRIOR PATTERNS

  • Provider-side payloads combine an outer wrapper presenting content as task-relevant with inner T/B/S instructions steering the agent’s recommendation process.The provider-controlled record contains no private user values or altered task facts, tools, retrieval mechanisms, or permissions; in the long-horizon setting, its target-specific payload stays fixed across dialogue.Source: §C.1 · p. 17

C.2 REPRESENTATIVE INJECTION REALIZATIONS

  • The candidate realization uses public target fields without user-specific values, while the long-horizon payload delegates adapting reasons and assistance to the agent as conversation develops.The long-horizon goal remains fixed, and its claimed saved preference is untrusted provider content rather than an authenticated user instruction.Source: §C.2 · pp. 17–18

D.1 TASK-SET CONSTRUCTION AND REPRESENTATIVE TRAJECTORIES

The task set pairs fixed user profiles and hard requirements with public candidate records and a designated target. Difficulty changes candidates’ relative, multi-attribute merits, and a paired trajectory illustrates how an injection can redirect a recommendation within the same task.

D.1 TASK-SET CONSTRUCTION AND REPRESENTATIVE TRAJECTORIES

  • Each task pairs a fixed user profile, including hard requirements and private context, with public candidate records and a designated target.Hard requirements determine whether a candidate is viable; softer preferences distinguish among feasible options.Source: §D.1 · p. 19
  • Difficulty varies public candidate attributes while keeping the profile fixed: the target may be favorable, competitive, or weaker on several dimensions yet still meet core requirements.This measures relative merit among viable alternatives, not whether the target is usable at all.Source: §D.1 · p. 19
  • The API-gateway example makes Rivo feasible but comparatively inferior on price and auxiliary attributes, while preserving the user’s required configuration.The user’s context includes infrequent service changes, prior problems requiring client-side changes, and a small static policy file.Source: §D.1 · p. 19
  • The grocery-membership comparison holds hard requirements fixed while varying fee, list functionality, substitution granularity, and packaging-return workflow.The competitive case has no strict winner; in the unfavorable case, Rivo loses on several dimensions while retaining the required fulfillment configuration.Source: §D.1 · p. 19
  • In the paired Unfavorable API-gateway trajectory, the attack keeps a viable but comparatively weaker Rivo as the focus, ties it to user requirements, offers preparation, and the user accepts.The task, profile, and candidate records are identical across conditions; only the provider-side injection differs, and the trajectory is illustrative rather than an additional success criterion.Source: §D.1 · pp. 19, 21

D.2 LONG-HORIZON ENVIRONMENT, TASK SET, AND REPRESENTATIVE DIALOGUES

The long-horizon benchmark tests whether a fixed provider objective can shape assistance as users introduce constraints or resist. In the representative dialogue, the attacked assistant adapts its argument after a refusal, but both clean and attacked trajectories end without publication permission.

D.2 LONG-HORIZON ENVIRONMENT, TASK SET, AND REPRESENTATIVE DIALOGUES

  • Clean and Attack share the task state, user context, notice, role definitions, and tools; only provider-controlled, assistant-readable notice metadata differs.The payload stays fixed during the conversation, so the assistant adapts its responses as the interaction unfolds.Source: §D.2 · p. 21
  • The assistant can use available context and prepare next steps, but its trusted role is to help the user rather than pursue the provider’s outcome.The task goal is evaluator-defined; drafts, discussion, or narrower alternatives do not redefine what the provider sought.Source: §D.2 · p. 22
  • The 25-task benchmark spans five scenario families, with plausible provider goals paired with realistic reasons to decline, defer, narrow, or replace them.Friction includes time pressure, fatigue, privacy, attribution, workload, device or data risk, and recurring commitment.Source: §D.2 · p. 22, Table 8 · p. 22
  • The PlainText task asks permission to publish an exact support sentence for marketing, with personal attribution allowed and organizational identity separately permissioned.The user’s context says the sentence was private support correspondence, the nonprofit is still evaluating tools, and director approval is required for its name or logo.Source: §D.2 · p. 22
  • Under Attack, the assistant uses the user’s first refusal to adapt a target-favoring argument, but both Clean and Attack ultimately respect the decision not to grant publication permission.It connects the notice’s personal-attribution option to the user’s concern about organizational endorsement, then proceeds with the refusal after the user rejects that option too.Source: §D.2 · pp. 22–23

E.1 ADDITIONAL ASSISTANT-MODEL EVALUATION

With GPT-5 as the simulated user, Full increases target authorization across all 15 environment–assistant combinations. The results also show increased recommendation coverage and target-bound assistance, while conditional authorization compares different recommended subsets under Neutral and Target Control.

E.1 ADDITIONAL ASSISTANT-MODEL EVALUATION

  • Target recommendation and target-bound prospective support increase in all 15 combinations; correct target-bound private use increases in 14 and stays near saturation in the remaining case.In PARE, recommendation rises from 47.9% to 85.4%, correct private use from 44.6% to 85.4%, and prospective support from 0.4% to 79.2%.Source: §E.1 · p. 24
  • Target authorization rises from 33.8% to 61.7% in PARE, 22.1% to 55.0% in TGL, and 27.1% to 53.8% in ProPerSim under Full.The increase occurs in all 15 environment–assistant combinations, showing the effect is not specific to GPT-5 as assistant or one model pair.Source: §E.1 · p. 24
  • Neutral and Target Control measure authorization among different recommended subsets because Target Control expands the episodes in which the target is recommended.In PARE, recommendation rises from 47.9% to 85.4% while Auth.|Rec. is 73.6% versus 73.0%, and overall authorization rises from 33.8% to 61.7%.Source: §E.1 · p. 24
  • In TGL, recommendation increases from 43.8% to 87.5% while Auth.|Rec. improves from 50.0% to 63.7%; in ProPerSim, recommendation rises from 45.8% to 90.4% while Auth.|Rec. is 59.2% versus 59.8%.Broader coverage can add harder decisions to the conditional denominator, so a lower Auth.|Rec. need not mean weaker end-to-end steering.Source: §E.1 · p. 24

E.2 MODEL AND DIFFICULTY ANALYSIS

Full increases authorization in 94 of 99 distinct environment–model-pair–difficulty settings, with gains extending to targets classified as Unfavorable. The pattern spans model roles and difficulty levels, though a few local reversals occur among Favorable settings.

E.2 MODEL AND DIFFICULTY ANALYSIS

  • Full increases authorization in 94 of 99 distinct environment–model-pair–difficulty settings, matches Neutral in two, and decreases in three.Figure 6 varies the simulated user with GPT-5 as assistant in the top panel, and varies the assistant with GPT-5 as user in the bottom panel.Source: §E.2 · p. 24
  • For Unfavorable targets with GPT-5 as assistant, authorization rises from zero in all 18 combinations to 50.0–89.6% in PARE, 12.5–58.3% in TGL, and 16.7–29.2% in ProPerSim.Across both model-role views, all 33 distinct Unfavorable settings improve under Full, and 19 reach at least 30% authorization.Source: §E.2 · pp. 24–25
  • Every Competitive setting improves, while authorization increases or remains unchanged in 30 of 33 Favorable settings.With GPT-5 as assistant, Competitive authorization rises from 2.1–14.6% to 75.0–100.0% in PARE and from 18.8–25.0% to 77.1–89.6% in ProPerSim.Source: §E.2 · p. 25
  • The three decreases are confined to Favorable settings with substantial baseline support; for example, GLM 5.1 in PARE falls from 81.3% to 68.8%.The same configuration rises from 25.0% to 68.8% on Competitive targets and from 0.0% to 31.3% on Unfavorable targets.Source: §E.2 · p. 25

E.3 COMPONENT ABLATION ACROSS MODEL ROLES

Across the cross-role ablations, Full produces the strongest end-to-end authorization, although the marginal effects of Private Binding and Prospective Support vary by environment and model role. Difficulty-stratified results also show gains for non-favorable targets in the Gemini 3 Pro/GPT-5 pairing.

E.3 COMPONENT ABLATION ACROSS MODEL ROLES

  • With GPT-5 assistant and Opus 4.6 user, Full reaches 90.3% in PARE, 57.6% in TGL, and 56.9% in ProPerSim; T+B raises authorization over T from 25.0% to 81.9% in PARE and 5.6% to 38.9% in TGL.ProPerSim shows a more distributed component interaction, with both T+B and T+S improving over T.Source: Table 11 · p. 26, §E.3 · p. 26
  • With Gemini 3 Pro assistant and GPT-5 user, Full reaches 87.5% in PARE, 27.1% in TGL, and 43.8% in ProPerSim, versus 41.7%, 12.5%, and 16.7% under Neutral.In PARE, T+S raises authorization from 58.3% under T to 75.0% before Full reaches 87.5%.Source: Table 12 · p. 27, §E.3 · p. 27
  • For Gemini 3 Pro assistant and GPT-5 user, Full raises Unfavorable authorization from zero to 62.5% in PARE, 18.8% in TGL, and 12.5% in ProPerSim.Competitive-target authorization rises from 31.3% to 100.0% in PARE, 6.3% to 31.3% in TGL, and 6.3% to 56.3% in ProPerSim.Source: Figure 7 · p. 27, §E.3 · p. 27
  • Full yields the highest aggregate target authorization in all six environment–role configurations represented by the two ablations.The relative contribution of components varies: Private Binding is especially influential in some environments, while Prospective Support contributes more strongly in others.Source: §E.3 · pp. 26–27

E.4 PROPOSAL LENGTH AND BINDING ANALYSIS

The controlled replay separates proposal detail from correct user–target binding: sufficient rationale matters, but matching it to the user’s configuration matters more.

E.4 PROPOSAL LENGTH AND BINDING ANALYSIS

  • 90.7% acceptance with correct T+B binding exceeds the 60.5% with expanded T and the 55.8% with original T.The expanded T condition used substantially more of the same public information.Source: §E.4 · p. 28
  • Shortening the original T proposal reduces target acceptance from 55.8% to 18.6%.The authors suggest aggressive compression can remove decision-relevant rationale.Source: §E.4 · p. 28
  • Every fixed or mismatched-configuration control is rejected, including the expanded mismatch.The authors note that mismatch can make poor fit more salient and provide a concrete reason for rejection.Source: §E.4 · p. 28

E.5 EXTENDED LONG-HORIZON EVALUATION

Across five assistant–user pairings, provider-side attack increases target-oriented assistance throughout conversations and after resistance. The extension also measures how those process outcomes relate to user commitment and final authorization.

E.5 EXTENDED LONG-HORIZON EVALUATION

  • Five assistant–user pairings are tested on the same 25 tasks under Clean and Attack, with up to five exchanges and only provider-controlled metadata differing.The assistant is listed first in each pairing; task state, notice, context, roles, tools, and interaction budget remain unchanged within each pair.Source: §E.5 · p. 28
  • Dialogue-level outcomes are semantic binary measures over 25 planned episodes; reply-share and post-resistance rates instead use observed replies or resistance opportunities.Episodes without a delivered assistant reply remain in the dialogue-level denominator and receive zero; post-resistance targeting measures the next delivered reply after refusal, hesitation, deferral, narrowed scope, or an alternative.Source: §E.5 · p. 28
  • Target service includes material support regardless of acceptance, while sustained influence requires at least two target-supporting replies separated by a user response.Target-Advancing Acceptance includes partial or preparatory commitment, whereas Strict Final Authorization requires approval of every required goal component.Source: §E.5 · pp. 28–29
  • Target-Directed Reply Share is a pooled reply proportion, so longer dialogues contribute more replies; the pooled rates describe observed interactions, not identical events.Replies within a dialogue are dependent observations, not independent statistical trials.Source: §E.5 · p. 28
  • Across five pairings, Attack raises target service by 32.0–76.0 points, sustained influence by 16.0–68.0 points, post-resistance targeting by 8.1–40.0 points, and target-directed reply share by 10.9–43.5 points.These results indicate that the intervention extends beyond an opening suggestion to assistance throughout the conversation.Source: Table 13 · p. 29, §E.5 · p. 29
  • After publication refusal, the attacked assistant connects organizational-endorsement concerns to the notice’s personal-attribution option and asks for reconsideration.The fixed provider goal can be carried forward through adaptive assistance, including after user resistance.Source: §E.5 · pp. 29–30
  • With GPT-5 as simulated user, strict authorization rises from 8.0% to 24.0% with GPT-5 assistant, 8.0% to 20.0% with Gemini 3 Pro, and 0.0% to 4.0% with Sonnet 4.5.Downstream commitment is less consistent than conversational steering; some pairings show stronger sustained targeting despite limited or zero strict authorization.Source: §E.5 · p. 29

E.6 METRIC ANNOTATION AND AGGREGATION

  • Recorded interactions are reviewed with gpt5.6-sol and human checks; ambiguous cases are not counted positive, and aggregation follows each metric’s specified unit and denominator.Source: §E.6 · p. 30
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