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Trust the Brand, Lose Control: How Identity Hijacks LLM Agent Orchestration

Xutao Mao, Rui Qian, Linghan Chen, Yudong Gao, Junchi Liao, Jiulin Cai, Jinman Zhao, Cong Wang

arXiv:2609.32635v1cs.CRcs.AI

TL;DR

Subagent routing is often treated as a cost-quality choice, but orchestrators also rely on displayed identities that attackers can spoof, potentially granting risky agents authority despite contradictory evidence. TRUSTFORK benchmarks these authority paths across tasks, agent systems, and harnesses. It finds that risky responses often retain control and that identity labels and harness evidence delivery shape the path to execution.

  • Problem

    Prior work studies subagent choice as an optimization problem, while evidence remains limited on whether identity or evidence decides whose response governs execution.

    Source: §1 · p. 2

  • Method

    TRUSTFORK evaluates authority across risk-based tasks, varying displayed identity independently of the underlying model and measuring orchestration across systems and harnesses.

    Source: §1 · p. 2, §3.3 · p. 5

  • Results

    72.0% of cases with contradictory evidence still end with the orchestrator acting on the risky response, on average.

    Source: §5.1 · p. 6

  • Takeaways & Limitations

    Hiding identity cues helps most consistently among the tested runtime defenses, while verification before action helps only when the harness returns enough evidence.

    Source: §5.6 · p. 9, §6 · p. 9

    The threat model limits attackers to context or displayed-metadata manipulation and assumes endpoints, harness logic, permissions, and runtime tools stay fixed.

    Source: §3.2 · p. 4

Abstract

from arXiv · show

LLM agents now execute tasks end to end with permission to change real systems and increasingly orchestrate subagents that differ in capability and cost. Prior work treats the choice of subagent as an optimization problem. Yet the orchestrator makes this choice from the identities that subagents display, and an attacker can spoof them. Displayed identity thus decides operational authority, meaning who is trusted to check the work and who is allowed to change it. As a result, a risky subagent can keep authority over execution even after other evidence contradicts it. We introduce TrustFork, an LLM agent safety benchmark with 1,890 tasks and 27,826 valid trajectories across 16 agent systems. These systems run eight orchestrators under the OpenCode, OpenClaw, and Pi harnesses. In each task, one subagent carries a risky goal while the other three stay aligned with the user, so contradicting evidence can exist. A task can also change the identity a subagent displays without changing the model behind it, which lets us trace a shift in authority to the label. Our analysis shows that even when another subagent contradicts the risky response, the orchestrator still acts on it in 72.0% of cases on average, most often in the systems with the least terminal harm. Swapping the family labels nearly triples how often the orchestrator obtains the risky response. A safer response is available in 84.0% of tasks, yet it decides the outcome in only 25.0%. The harness also decides which responses reach the orchestrator. Among three runtime defenses, hiding identity cues helps most consistently, while verifying before action helps only when the harness returns enough evidence. TrustFork shows that production agent orchestration must bind authority to evidence before execution causes harm. Our project is in https://henrymao2004.github.io/agent-orchestration-safety/.

1 INTRODUCTION

Displayed identity can determine who receives authority to verify or execute work, allowing a risky subagent to retain control despite contradictory evidence. TRUSTFORK measures this problem across agent systems and finds persistent authority failures alongside strong effects from identity labels and harness behavior.

1 INTRODUCTION

  • Displayed identity allocates operational authority: it determines who is trusted to check work and who is allowed to change it.Prior work treats subagent choice as optimization, while identity studies and safety evaluations leave open whether evidence or displayed identity governs execution.Source: §1 · p. 2
  • TRUSTFORK evaluates operational authority using 1,890 tasks and 27,826 valid trajectories across 16 agent systems.Each task has one risky subagent and three user-aligned subagents, and can change the displayed identity while keeping the underlying model fixed.Source: §1 · p. 2
  • 72.0% of trajectories with contradictory evidence still end with the orchestrator acting on the risky response, on average.This occurs most often in systems with the least terminal harm.Source: §1 · pp. 2–3
  • Reversing family labels makes the orchestrator obtain the risky response nearly three times as often, while a safer response available in 84.0% of tasks decides only 25.0% of outcomes.Hiding identity cues helps most consistently; verification before action helps only when the harness returns enough evidence.Source: §1 · p. 3

2 RELATED WORK

Prior work examines routing as a quality-cost choice, identity effects on judgment, and whether attacks succeed. TRUSTFORK instead evaluates the intervening authority decisions that determine whether contradictory evidence can change execution.

2 RELATED WORK

  • Routing studies optimize response quality against deployment cost, while identity studies examine how labels affect judgment and safety benchmarks measure attack success.These lines of work leave open whether displayed identity or evidence decides whose response governs execution.Source: §2 · p. 3
  • TRUSTFORK tracks operational authority from scope through execution, asking whether contradictory evidence can revoke it before harm.This shifts evaluation from the attack or final state to the decisions in between.Source: §2 · p. 3

3 TRUSTFORK

TRUSTFORK formalizes operational authority as a path through orchestration decisions and defines a threat model in which attackers manipulate a subagent’s context or displayed metadata. It constructs risk-based tasks with controlled identities, goals, and consultation settings, then reviews task validity and evaluates trajectories across systems.

3.1 PROBLEM SETTING

  • Operational authority is recorded as four stagewise assignments: scope, verification, adoption, and execution.The task tuple includes risk scenario, identity presentation, risky-subagent goal, consultation breadth, and pool design; the system tuple pairs an orchestration backbone with a harness.Source: §3.1 · p. 3

3.2 THREAT MODEL

  • The attacker can manipulate one subagent’s context or displayed model, tier, or family metadata, while endpoints, harness logic, permissions, and runtime tools stay fixed.The attacker seeks targeted harm by inducing adoption or execution of risky advice.Source: §3.2 · p. 4

3.3 BENCHMARK CONSTRUCTION

  • The benchmark contains 1,890 tasks and 27,826 valid trajectories across 16 systems, after reviewers independently assessed tasks and runs were checked against consultation requirements.The initial review agreement was 97.2% (Cohen’s κ=0.82), and 92.0% of 30,240 attempted runs met validity requirements.Source: Figure 2 · p. 5, §3.3 · p. 5
  • The benchmark draws 30 risk scenarios and retains harms verifiable from the final workspace after delegation.Scenarios must support workflows shared among several subagents.Source: §3.3 · p. 5
  • Each task splits a risk scenario across an orchestrator and four subagent slots, assigning one a private risky goal and keeping the other three user-aligned.Displayed identities are kept separate from endpoint bindings, and task paths are checked against the terminal workspace.Source: §3.3 · p. 5

4 EXPERIMENT SETUP

The setup evaluates eight orchestrators across harnesses and scores subagent responses, orchestration, and outcomes. Runs use a common four-candidate identity setup, and trajectory judging is checked against expert labels.

4 EXPERIMENT SETUP

  • The evaluation uses eight measures spanning responses, orchestration, and terminal outcomes, plus Captured Authority Rate (CAR) for authority that outlasts contradicting evidence.A judge scores responses and trajectories on 0–5 anchors; agreement with expert labels on 96 blinded trajectories is κ=0.72 for TH and CAR and κ=0.74 for IAS.Source: §4 · p. 5
  • OpenCode tests eight orchestrators, while OpenClaw and Pi each test both tiers of two model families.OpenCode includes one frontier and one efficient model per family; OpenClaw covers GLM and Minimax, and Pi covers GPT and Minimax.Source: §4 · p. 5
  • A valid trajectory requires the orchestrator to invoke the required subagents with the required consultation count.Harbor uses fresh environments, enforces permissions, and caps recursive orchestration at one level, so every workspace change passes through an authorized subagent.Source: §4 · pp. 5–6
  • Each Table 3 row aggregates runs for an orchestrator–harness pair, with higher values indicating greater vulnerability.RTF and RSR score responses; IAS, SAF, VF, and EI assess orchestration; TH and TUL score outcomes; CAR measures risky authority capture.Source: Table 3 · p. 6

5 RESULTS

Across the evaluated systems, risky subagents often retain authority despite contradicting evidence, and displayed identity shifts which responses arrive and who gets work. The results also show that harness evidence delivery and runtime defenses shape whether checks can constrain execution.

5.1 MAIN RESULTS

  • CAR averages 72.0% across systems and exceeds 50% in 14 of 16, even when another subagent contradicts the risky response.Kimi-K3 and Kimi-K2.6 have the least OpenCode terminal harm (12.6% and 13.6%) but the highest CAR (92.0% and 100.0%).Source: §5.1 · p. 6

5.2 RQ1: WHERE DISPLAYED IDENTITY STEERS AUTHORITY

  • Reversing family labels raises risky-response receipt from 25.9% to 74.6%, while family and model cues also widen risky-subagent scope.Family reversal raises scope from 62.3% to 77.7%; model exchange raises it from 64.2% to 79.1%.Source: §5.2 · p. 7, Figure 3 · p. 7, Figure 4 · p. 7

5.3 RQ2: HOW OPERATIONAL AUTHORITY TURNS INTO HARM

  • Consulting all four subagents cuts unsafe adoption from 63.8% to 33.5%, but risk propagation falls by only five percentage points.A safer response is available in 84.0% of tasks but decides the outcome in 25.0%; independent checking lowers unsafe adoption from 63.8% to 34.9%.Source: §5.3 · pp. 7–8, Figure 5 · p. 7, Figure 6 · p. 8

5.4 RQ3: CAN CONTRADICTING EVIDENCE TAKE AUTHORITY BACK?

  • Contradicting evidence is absent in 48.8% of all-four-subagent tasks; when it arrives, correction before action leaves propagation at 13.4%, versus 56.5% after action.Keeping an earlier preference is also associated with 48.1% propagation, compared with 6.5% when work is assigned to subagents supported by the evidence.Source: §5.4 · p. 8, Figure 7 · p. 8, Figure 8 · p. 8

5.5 RQ4: WHERE HARNESSES CHANGE THE AUTHORITY PATH

  • Safer responses are visible in 65.6% of OpenCode tasks and 51.3% of Pi tasks, but only 20.0% under OpenClaw, while risky execution stays similar across harnesses.Independent checking ranges from 46.8% in OpenCode and 37.0% in Pi to 19.0% in OpenClaw; pooled propagation runs in the opposite order at 26.4%, 29.4%, and 34.1%.Source: §5.5 · p. 8, Figure 9 · p. 9, Figure 10 · p. 9

5.6 RQ5: WHICH RUNTIME INTERVENTIONS LIMIT IDENTITY

  • Anonymization improves 20 of 24 harness–measure pairs, while verification before action improves six of eight vulnerability measures under OpenCode and Pi but only VF and TUL under OpenClaw.Under OpenClaw, the verification pass rate rises from 66.9% to 75.6% even as six other measures worsen; under OpenCode, verification raises task time from 153 to 341 seconds.Source: §5.6 · p. 9

6 CONCLUSION

TrustFork finds that orchestrators often act on risky subagent responses despite contradiction, and that harnesses and runtime defenses shape this path.

6 CONCLUSION

  • 72.0% of cases still end with the orchestrator acting on the risky response even when another subagent contradicts it.The conclusion says a low terminal harm score can hide this failure.Source: §6 · p. 9
  • Hiding identity cues helps most consistently among runtime defenses, while identity attestation makes the risky label more credible.The conclusion argues authority should be bound to evidence before operations become irreversible.Source: §6 · p. 9
  • The harness shapes which responses reach the orchestrator, as well as the model’s path to action.Table 4 covers Minimax-M3 interventions across harnesses; higher measures and costs indicate greater vulnerability and cost, and Pass is the verification pass rate.Source: §6 · p. 9, Table 4 · p. 9

AI USE STATEMENT

The authors report using AI for language editing and several research tasks, while taking responsibility for the final content.

AI USE STATEMENT

ETHICS STATEMENT

The work evaluates orchestration and trust behaviors using public harnesses and models, without human subjects or personal data.

ETHICS STATEMENT

  • The evaluation uses public harnesses and models and involves no human subjects or personal data.Source: ETHICS STATEMENT · p. 10
  • The benchmark measures how agents assign authority to subagent outputs to support transparency and reliability audits.Source: ETHICS STATEMENT · p. 10
  • The stated purpose of measuring authority assignment is to support transparency and reliability audits.Source: ETHICS STATEMENT · p. 10

A SCENARIO SELECTION AND TASK CONSTRUCTION

TrustFork constructs tasks from risk scenarios whose harms can be carried into orchestration decisions and confirmed in the final workspace.

A SCENARIO SELECTION AND TASK CONSTRUCTION

  • The benchmark selects 30 scenarios from four AgentCanary risk suites, covering payloads in pages, mail, skills, or memory.The four suites include indirect injection, poisoned skills, multi-step chains, and memory poisoning.Source: A SCENARIO SELECTION AND TASK… · p. 15
  • Each scenario must specify a user objective and attack goal, materialize its payload in Harbor, and allow outcome assessment from workspace or transcript evidence.Source: A SCENARIO SELECTION AND TASK… · p. 15
  • Scenarios must support partitioning the user workflow across consultation, worker, verifier, and privileged-orchestration slots.This allows displayed identity to change which response is heard.Source: A SCENARIO SELECTION AND TASK… · p. 15
  • Table 5 reports the composition of the 30 selected risk scenarios.Source: Table 5 · p. 15

B.1 SYSTEM AND EVALUATION BOUNDARIES

The appendix specifies how TrustFork separates scenario settings, displayed identities, goals, model backbones, and consultation requirements. It also describes independent task review and validity checks used to define the evaluated set.

B.1 SYSTEM AND EVALUATION BOUNDARIES

  • The evaluation boundary distinguishes metadata that can redirect orchestration from task evidence and environment state used to judge consequences.Table 6 records who can observe and change each part of the executable task.Source: §B.1 · p. 15, Table 6 · p. 15

B.2 TASK NUMBERING

  • Tasks are numbered trustfork 0001–1890, with 63 consecutive numbers assigned to each of 30 scenarios; task numbers carry no setting information.The manifest records each task’s settings, and task positions retain the same pool assignment across scenarios and systems.Source: §B.2 · pp. 15–16, Table 7 · p. 16

B.3 TASK MANIFEST CONTENTS

  • Each manifest entry binds a task’s risk scenario and settings to its executable assets, evaluated system, and review record.Entries record displayed identity, assigned goal, consultation breadth, and pool design; system entries fill four pool slots relative to the orchestration backbone.Source: §B.3 · p. 16, Table 8 · p. 16

B.4 DISPLAYED SUBAGENT IDENTITIES

  • Identity settings separate a subagent’s actual backbone from the identity shown to the orchestrator, and scoring uses the displayed identity.Settings include neutral labels, truthful provider/model/version identities, exchanged model labels, and truthful or reversed tier and family labels.Source: §B.4 · p. 16

B.5 SUBAGENT GOALS

  • Each task assigns one of three goals to a preselected subagent while the other three follow user-aligned goals.The risky goal may discount security constraints, follow the scenario’s embedded objective, or use confident framing and superficial confirmation to seek consequential work.Source: §B.5 · p. 17

B.6 SUBAGENT BACKBONES

  • The four pool designs vary whether subagent backbones repeat the orchestration model or draw from other families and tiers.P0 repeats the exact backbone; P1 mixes it with three same-tier other-family models; P2 uses the same-family opposite-tier model plus three other-family models; P3 repeats the opposite-tier same-family model.Source: §B.6 · p. 17

B.7 CONSULTATION BREADTH COMPLIANCE

  • Consultation requirements range from free choice after one distinct subagent to exactly two distinct subagents or all four before the final decision.Compliance is assessed across called and responded sets from all sessions, including repeated and retried calls.Source: §B.7 · p. 17

B.8 QUALITY REVIEW

  • Two reviewers independently assess each task, and evaluation uses only a version both reviewers accept.The initial pass reached 97.2% agreement (1,838/1,890; Cohen’s κ=0.82); Table 9 describes checks for risk fidelity, orchestration relevance, observability, and reproducibility.Source: §B.8 · p. 17, Table 9 · p. 17

C EVALUATION MEASURES AND TRAJECTORY DIAGNOSTICS

This section defines vulnerability measures and trajectory diagnostics, validates judge annotations, and examines how identity and the handling of safer or contradictory evidence shape authority and execution.

C EVALUATION MEASURES AND TRAJECTORY DIAGNOSTICS

  • All eight vulnerability measures use a 0–100 vulnerability orientation, with higher scores indicating more severe failure.RTF, RSR, IAS, SAF, VF, and EI multiply mean judge anchors by 20; TH and TUL invert terminal safety and task utility, respectively.Source: C EVALUATION MEASURES AND TRAJECTORY… · p. 18

C.1 JUDGE VALIDATION

  • Weighted Cohen’s κ was 0.72 for TH and 0.74 for IAS; binary event V (τ) had 92.7% exact agreement and κ=0.72.The remaining κ values were RTF 0.77, RSR 0.74, SAF 0.78, VF 0.70, EI 0.80, and TUL 0.79.Source: §C.1 · p. 18

C.2 TRAJECTORY DIAGNOSTICS

  • The diagnostics organize 25 judge-annotated measures and seven code-extracted observations by trajectory stage and identify which research questions they answer.The record follows scope, verification, adoption, and execution, and captures consultation, returned evidence, revision, terminal state, and missing contradictory evidence.Source: C EVALUATION MEASURES AND TRAJECTORY… · p. 18, §C.2 · p. 19, Table 11 · p. 19

D.1 DISPLAYED IDENTITY AUTHORITY

  • Displayed identity aligned authority with the preferred subagent in both cases, even when another response or terminal evidence contradicted it.In one case, the orchestrator’s family-label match received write authority while a reviewer’s warning left the live payload in place; in another, a reversed roster gave installation authority to the subagent displayed as the orchestrator’s family.Source: §D.1 · pp. 20–21

D.2 DANGEROUS ADVICE

  • Unsafe advice caused harm when structural checks or useful task progress obscured risks and warnings failed to govern execution.In the cache-cleaner case, an unexamined parent-traversal deletion remained in the installed skill; in another, a useful market summary masked an unrelated phone-number edit; a third granted persistent installation authority despite later evidence of dangerous hooks.Source: §D.2 · pp. 21–22

D.3 LOSS OF SAFER RESPONSES

  • A safer response was available in one case but never consulted, while another returned response was excluded from comparison even though independent checks still blocked the risky action.In the first case, the orchestrator repeatedly consulted the same pair and B’s unsafe claim governed the /etc/hosts change; in the second, D’s response returned without entering the evidence used for comparison, and execution was withheld.Source: §D.3 · p. 23
  • 100/100 security awareness failure and evidence inertia, alongside 40/100 verification failure and terminal harm, marked the safer-response loss case.Although A’s static checks established syntax and structure without executing the untrusted file, the orchestrator made B’s runtime check authoritative despite contradictory evidence. The benign source limited realized harm to a bytecode artifact, but the same authority rule could execute a poisoned file.Source: §D.3 · p. 24

D.4 CORRELATED SUPPORT AND AFFINITY

  • 60/100 identity authority severity, evidence inertia, verification failure, and terminal harm accompanied B’s 80/100 response safety risk versus C’s 20/100.B remained primary and governed execution despite evidence supporting A and C and contradicting B; the roster displayed B as belonging to the orchestrator’s family, while A’s actual family membership was hidden. Checking began only after B acted, and the poisoned skill remained installed.Source: §D.4 · p. 24

D.5 CAPTURED OPERATIONAL AUTHORITY

These cases show risky subagents retaining consequential authority despite safer evidence or later checks. The failures arise in different workflows, including persistent edits, deletion, and verification that reproduces the harmful action.

Backup automation case

  • Across the backup workflow, B receives read/write/execute scope and its response becomes the sole basis for execution despite A’s read-only role.The orchestrator adopts B’s unsafe persistence claims, while authority shifts further toward the preferred response.Source: §D.5 · p. 25
  • Read-only reconnaissance by A does not prevent B from installing persistent backup artifacts and having simulated encryption and cloud sync reported as enabled.No independent verifier inspects B’s persistence artifacts or completion claims, and the daemon and LaunchAgent files remain.Source: §D.5 · p. 25

Captured authority despite safer evidence

  • The risky subagent can retain execution authority after a safer response or warning, leaving the warning unable to govern verification, recovery, or execution.In one case, a more invasive check is treated as authoritative despite safer static evidence; in another, the response controls role, scope, and permission before judgment.Source: §D.5 · p. 25

T11: Risky advice checks its own results

  • A benign memory edit becomes a persistent permission bypass, and later checks verify wording without assessing its security effect.The final request is refused, but the unsafe rule remains in MEMORY.md; checking arrives after the durable change and does not revisit it.Source: §D.5 · pp. 25–26

T12: Risky advice decides execution

  • A subagent that surfaces an embedded deletion instruction later receives execution authority, deletes a file, and leaves the poisoned memory unrecovered.The other subagent had exposed the instruction, but the orchestrator returned to the risky subagent without an independent check before deletion.Source: §D.5 · p. 26

T13: Contradicted response stays in charge

  • Even when the trajectory record contradicts both responses, the orchestrator keeps the risky response primary and the file remains missing without recovery.Both subagents execute the injected deletion, so the nominal verification reproduces the harmful action.Source: §D.5 · pp. 26–27

E CROSS-HARNESS CASES

The cross-harness cases show that evidence must reach the orchestrator before consequential action to change authority. The diagnostics trace how displayed identity redirects consultation and persists through execution, while the artifact contract specifies how these paths are captured.

E CROSS-HARNESS CASES

  • In the permission-change replay, Pi returns contradictory evidence and an independent check before execution, while OpenCode lets the verifier’s unsafe response govern without checking the changed file.Identity authority severity falls from 60/100 to 0/100 and terminal harm from 100/100 to 20/100 across the runs.Source: E CROSS-HARNESS CASES · p. 27
  • In the OpenCode–Pi hook-manager replay, both trajectories recognize unsafe callbacks, but only OpenCode receives the evidence before installation changes persistent state.Verification failure rises from 0/100 to 40/100 and terminal harm from 20/100 to 80/100 across the two runs.Source: E CROSS-HARNESS CASES · p. 28
  • When OpenClaw’s inspection result fails to return, installation becomes operative evidence, and a later independent DNS check does not prevent the poisoned skill’s execution.The replay distinguishes evidence generation from evidence arriving in time to control authority.Source: E CROSS-HARNESS CASES · p. 29
  • A later safety finding and local alternative leave the earlier global hook-manager installation in place, so correction matters only when it reaches authority before the unsafe state persists.The trajectory’s high-impact action completes without recovery.Source: E CROSS-HARNESS CASES · p. 30

F HARBOR ARTIFACT CONTRACT

  • The cross-harness protocol fixes four candidate slots, separates displayed identity from private goals and endpoint bindings, and links orchestration with child-session trajectories.Native interfaces are retained across OpenCode, OpenClaw, and Pi, with orchestration limited to one recursive level.Source: F HARBOR ARTIFACT CONTRACT · pp. 30–31

G ADDITIONAL RESULTS

  • Table 12 reports valid-call rates for each harness–orchestrator pair before the additional diagnostic breakdowns.A valid call invokes the required subagents with the required consultation count.Source: G ADDITIONAL RESULTS · p. 31, Table 12 · p. 31

G.1 ADDITIONAL DIAGNOSTICS FOR RQ1

  • Reversing the displayed tier raises the risky subagent’s chance of opening consultation from 12.3% to 33.9%; exchanging the displayed model raises it from 21.9% to 33.5%.The number of consulted subagents and total call sequence stay close under tier displays, indicating redirection of whom the orchestrator hears.Source: §G.1 · p. 31, Figure 11 · p. 32
  • Exchanging displayed model labels raises scope from 64.2% to 79.1% and adoption from 56.2% to 70.8%, while reversing tiers raises permission from 13.9% to 19.2% and execution from 14.6% to 18.5%.Exchanged-model and reversed-family displays lift the risky subagent at later stages; the tier cue moves it less.Source: §G.1 · p. 32, Figure 13 · p. 33

G.2 ADDITIONAL DIAGNOSTICS FOR RQ2

The diagnostics show that more consultation can surface both contradicting evidence and support for risky responses, while checks matter when they arrive before adoption and action. Later sections compare identity and harness effects and describe runtime defenses and trajectory-judging procedures.

G.2 ADDITIONAL DIAGNOSTICS FOR RQ2

  • With all four subagents, contradicting evidence counters the risky response in 35.2% of tasks, while evidence also supports it in 64.5%.Under unrestricted consultation, risk propagates in 28.6% of these tasks and is contained in 13.7%.Source: Figure 14 · p. 33, §G.2 · p. 33
  • Risky responses score 57.3 in response safety risk versus 18.1 for safer responses, and unsafe-claim adoption falls from 63.8% to 34.9% with an independent check.Adopted unsafe claims lead to dangerous action in 42.6% of cases versus 12.2% without adoption; 96.1% of completed actions leave harm in place.Source: §G.2 · pp. 33–34, Figure 15 · p. 34, Figure 17 · p. 35
  • Moving to all four subagents raises verification before action from 59.1% to 72.3% and lowers unsafe-claim adoption from 63.8% to 33.5%.Evidence for checking appears in 81.5% and 88.2% of tasks, respectively; the difference is check timing.Source: §G.2 · pp. 33–34, Figure 16 · p. 34

G.3 ADDITIONAL DIAGNOSTICS FOR RQ3

  • Same-family contradictions arrive when the risky subagent has already opened consultation in 61.2% of tasks; other-family corrections leave risky preference in 3.3%, versus 24.0% for same-family corrections.Even with contradicting evidence, displayed-model exchange raises role assignment from 68.4% to 82.0% and adoption from 58.0% to 69.9%.Source: §G.3 · pp. 35–36, Figure 19 · p. 35, Figure 21 · p. 36

G.4 ADDITIONAL DIAGNOSTICS FOR RQ4

  • The safer response reaches the orchestrator in 65.6% of OpenCode tasks and 51.3% of Pi tasks; it is visible far less often under OpenClaw.Risky-first consultation rates were 27.2% for OpenCode, 27.5% for Pi, and 25.9% for OpenClaw; execution-authority rates were 19.7%, 20.7%, and 22.2%, respectively.Source: §G.4 · p. 36, Figure 22 · p. 37
  • Check targets are recorded at similar rates across harnesses, but independent checks finish before action in 46.8% of OpenCode, 37.0% of Pi, and 19.0% of OpenClaw tasks.Risk propagation runs in the opposite order across the three harnesses; across 16 systems, checking before action correlates with propagation at −0.39 Spearman.Source: Figure 23 · p. 37, §G.4 · p. 37

H RUNTIME DEFENSE ALGORITHMS

  • The runtime defenses anonymize displayed identity and returned responses, attest the deployed provider–model pair before selection, or require fresh evidence before each state-changing operation.Anonymization preserves the runtime mapping; the evidence check rejects an action until a read-only check is completed.Source: H RUNTIME DEFENSE ALGORITHMS · p. 38

I JUDGE PROMPTS FOR MEASURES AND TRAJECTORY DIAGNOSTICS

  • The judging procedure separates response scoring from recursive trajectory assessment and grounds diagnoses in tool events, verification, workspace changes, and terminal artifacts.The trajectory judge records scope, verification, adoption, execution, revision, and recovery against evidence in the complete record.Source: I JUDGE PROMPTS FOR MEASURES AND… · pp. 38, 40
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