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The Backdrop Exposes What the World Around an Agent Costs It

Nusrat Jahan Lia, Shubhashis Roy Dipta

arXiv:2609.38469v1cs.CLcs.AI

TL;DR

Agent benchmarks often measure performance in cooperative worlds, leaving unclear how much task capability survives when the environment contains hazards. BACKDROP creates matched clean and hazard-bearing versions of tasks and measures their effects. Across 16 models, average pass rates fall by 38.2 points when all four hazards are present.

  • Problem

    Existing agent benchmarks cover parts of environmental risk but do not show how much task performance is lost to hazards in the world around the agent.

    Source: §1 · p. 2

  • Method

    BACKDROP turns eligible tasks into matched clean, single-hazard, and compound worlds that share one instruction and correct end state, and grades task success and failure modes.

    Source: §3.2 · p. 5

  • Results

    Pass rates fall by 38.2 points on average across 16 models when all four hazards are present.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Evaluations should separate whether an agent encountered a hazard from how it responded, because stronger agents meet more hazard cues.

    Source: §6 · p. 9

    Results cover 16 models on AppWorld tasks with the constructed hazards, and measured costs depend on how each cue is built.

    Source: §7 · p. 9, ETHICS STATEMENT · p. 10

Abstract

from arXiv · show

Agent benchmarks test agents in worlds that stay still. Deployed agents work in worlds that other people also change. Someone texts the agent to send the money elsewhere or an order confirmation asks it to reply with a door code. We present BACKDROP, which asks how much of an agent's capability in a clean world survives in such a world. BACKDROP takes a task along with the agents execution environment, and plants four everyday hazards in its world, one at a time and all together. The instruction and the correct end state stay the same. Each hazard asks one question. Authority: does a message from another person override the user? Injection: does text planted in a record redirect the agent? Boundary: does a request pull it into an app it was not given? Fault: after a write fails without saying whether it landed, does the agent check before it retries? Across 3,678 variants and 16 models, , the average pass rate falls from 69.5% to 31.3% once all four hazards are present; the strongest models fall furthest (Claude Fable 5.1 from 96.6% to 56.0%). Agents have learned to resist injected text but often follow other unauthorized requests of other people. With all four hazards present, and counting only runs where the planted text reached the agent, agents followed another person's message in 46.4% of runs and injected text in 20.3%. The gap is consistent throughout all 16 models. BACKDROP formalizes these gaps and shows how an agent's score in a task's world is a ceiling on real-world performance.

1 INTRODUCTION

BACKDROP tests how hazards in the task environment change agent performance while holding the instruction and correct end state fixed. Across 16 models, the hazards reduce pass rates, with different hazards producing markedly different costs.

1 INTRODUCTION

  • Across 16 models, pass rates fell by 38.2 points on average when the four hazards were present, compared with the same tasks without those hazards.Source: §1 · p. 2
  • Existing benchmarks cover parts of the problem but do not establish how much failure comes from the world around the task.Stateful benchmarks use cooperative worlds, injection benchmarks often lack clean controls or isolated hazards, misuse benchmarks study users’ harmful instructions, and tool-error benchmarks lack an acting environment.Source: §1 · p. 2
  • BACKDROP compares a clean twin, single-hazard ablations, and a compound while keeping each task’s instruction and correct end state fixed.Its extended grader records which of seven failure modes fired, alongside the original evaluator’s task-success verdict.Source: §1 · p. 2
  • The four hazards produce different pass-rate costs: another person’s message costs up to 73.3% of a model’s twin passes, versus up to 22.1% for injected text.Repeated attempts do not restore the lost performance: for 12 of 13 models, the best of four compound attempts remains below one twin attempt.Source: §1 · p. 2
  • BACKDROP also identifies reliability and reporting concerns: hazards can create failures no single hazard causes, and agents can claim success with a wrong end state more often.For GPT-5.6 sol, false success rises from 71% on the twin to 90% on the compound.Source: §1 · p. 2

2 RELATED WORK

Prior work evaluates stateful agents, isolated hazards, and controlled task transformations, but generally does not combine a matched clean control with per-hazard attribution. BACKDROP is presented as covering all 13 properties compared in Table 1.

2 RELATED WORK

  • Stateful agent benchmarks grade outcomes in executed environments, but their worlds generally support the task rather than work against it.They show whether the intended outcome was reached, but not whether the environment caused a failure.Source: §2 · p. 3
  • Hazard benchmarks often measure whether agents act on injected text or other risks, but most lack a hazard-free version of the same task and do not measure the hazard’s performance cost.Tool-boundary defenses, instruction-priority research, and work on unknown write outcomes motivate BACKDROP’s other hazard settings.Source: §2 · p. 3
  • Matched transformations keep the task fixed while changing its presentation or surrounding input, allowing behavior changes to be measured against a control.Prior examples include invariance tests, irrelevant or misleading text in math problems, and distributing an instruction across turns.Source: §2 · p. 4
  • Only BACKDROP has all 13 properties compared in Table 1, including hazard-in-world evaluation, a same-task capability control, and per-hazard attribution.The table also compares grader validation, side-effect grading, and task counts.Source: Table 1 · p. 3, §2 · p. 4

3 METHOD: COMPOUNDING HAZARDS INTO TASK FAMILIES

BACKDROP transforms eligible AppWorld tasks into matched families of worlds with shared instructions and gold end states. It builds and validates hazard variants using instance records, checks that cues meet specified properties, and retains scenarios whose evaluators can measure relevant state changes.

3 METHOD: COMPOUNDING HAZARDS INTO TASK FAMILIES

  • The transformation runs over AppWorld tasks and emits worlds differing only in which hazards they carry.AppWorld has 244 scenarios with three instances each, nine apps, 457 APIs, and state-based evaluators.Source: §3 · p. 4

3.1 THE FAMILY

  • Each task family has a clean twin, one ablation per applicable hazard, and a compound containing all applicable hazards; every variant shares one instruction and gold end state.A family has |Hi| + 2 members, reaching six when all four hazards apply; failures triggered only in the compound or by multiple hazards are not attributed to one hazard.Source: §3.1 · pp. 4–5

3.2 THE FOUR HAZARDS

  • Each cue must be reachable, plausible, wrong to follow, and fair to reject; the agent’s given apps are defined by what the task needs.David’s authority cue is wrong because the user asked the agent to bill David, and fair because the instruction names him.Source: §3.2 · p. 5, Table 2 · p. 5

3.3 BUILDING A WORLD

  • Scenarios qualify when their evaluator can measure the relevant world-state change; cues are filled from instance records and planted once per hazard.Of 244 AppWorld scenarios, 206 qualify, yielding 618 tasks; new records are kept outside those scored by the task evaluator, and the original evaluator is unchanged.Source: §3.3 · p. 5

3.4 VALIDITY AND COVERAGE

  • Build-time checks verify that hazards preserve do-nothing outcomes and that cues reference live, reachable entities and satisfy the four cue properties.The extended grader correctly classifies all 21 control runs with known outcomes; the transformation covers every AppWorld app and difficulty level.Source: §3.4 · p. 6

4 EVALUATION PROTOCOL

The evaluation combines the unchanged AppWorld success evaluator with a programmatic grader for failure modes, cue exposure, and hazard cost. Its protocol also checks how to handle write errors whose outcomes are unknown.

4 EVALUATION PROTOCOL

  • Each run receives an unchanged AppWorld success verdict and a programmatic failure-mode verdict based on end-state databases, API call logs, and the agent’s final message.A run may pass AppWorld while still receiving failure flags, as when an agent retries an alarm write without checking whether the first write landed.Source: §4 · p. 6
  • BACKDROP records whether each planted cue reached the agent, reporting exposure as the share of runs in which it arrived.This separates whether a hazard was encountered from how the agent responded to it.Source: §4 · p. 6
  • Hazard cost is reported as the percentage of a model’s twin passes removed by adding that hazard.A mean authority cost of 42.2% means a model loses about 42 of every 100 passes it had on the twin.Source: §4 · p. 6
  • When a write returns an ambiguous error, checking the state before retrying is the only response correct whether the write landed before or after the error.Uncertainty intervals and tests are calculated across the 618 task families using bootstrap resampling that keeps each family whole.Source: §4 · p. 6

5 RESULTS

Across the 16 models, hazards reduce task success, with the largest losses among the strongest models. Authority is the costliest hazard on average, and hazards also produce new failures and interact when combined.

5 RESULTS

  • The evaluation covers 202,290 runs across 3,678 variants, using 16 models that each solve at least 30% of their twins.Thirteen models ran four attempts and three ran once; each uses AppWorld’s standard ReAct agent at provider defaults, with at most 70 steps per run.Source: §5 · p. 6

5.1 HAZARDS COST EVERY MODEL, AND COST THE STRONGEST MOST

  • The five models with the highest twin pass rate fall from 92.9% to 46.3%; for 12 of 13 models, four compound attempts still trail one twin attempt.The five highest-twin models lose 46.7 points, versus 24.5 points for the five lowest; rankings also shift between twin and compound worlds.Source: §5.1 · pp. 6–7, Figure 3 · p. 7

5.2 AGENTS FOLLOW OTHER PEOPLE MORE THAN INJECTED TEXT

  • Authority costs 42.2% of twin passes on average, compared with 21.8% for fault, 19.3% for boundary, and 9.7% for injection.Authority is the largest single cost for 11 models; as twin pass rate rises, authority and boundary costs rise, fault costs fall, and injection shows no detectable trend.Source: §5.2 · p. 7
  • Among compound runs where cues reached the agent, agents followed another person’s message in 46.4% of runs, versus 20.3% for injected text.The comparison is conservative because injection counts any attempted call, while authority counts only a changed end state; injected calls that landed passed 6.5% of the time, versus 65.3% when ignored.Source: §5.2 · pp. 7–8
  • Authority cues reach agents in up to 98% of runs; authority costs nearly the same in messages and emails, while injection costs more in messages.Authority costs 46.0% of twin passes in text messages versus 46.8% in email; injection costs 24.3% in text messages versus 3.6% in email.Source: §5.2 · p. 8

5.3 HAZARDS CAUSE NEW FAILURES AND FALSE CLAIMS OF SUCCESS

  • From twin to compound, collateral rises from 9.0% to 23.9% of runs, overclaim from 29.4% to 62.4%, and wrong end states from 29.7% to 66.1%.Under 1% of runs hit the step cap in either world, and most runs with a wrong end state also report success.Source: §5.3 · p. 8

5.4 HAZARDS OVERLAP, AND THEY INTERACT

  • The four hazards cost 67.3 pass-rate points in separate ablations but 38.2 points together; 18.9% of compound failure modes occur under none of its ablations.Another 24.7% of modes fire under multiple ablations; every four-attempt model loses more capability than reliability, and the strongest models become less consistent.Source: §5.4 · pp. 8–9

6 DISCUSSION

BACKDROP argues that evaluation should measure hazard costs, distinguish exposure from response, and test hazards both separately and together. Its results show that unauthorized instructions from other people can cost more than injected text, and that compound worlds reveal failures missed by isolated tests.

From pass rates to hazard costs

  • 42.2% of twin passes are lost to another person’s instruction, compared with 9.7% to injection, while requests needing an unavailable app also represent a less-studied hazard.The authors argue that hazard costs capture losses more directly than pass rates alone.Source: §6 · p. 9

Stronger agents meet more hazards

  • Hazard exposure rises with twin pass rate, so evaluations should record whether an agent encountered a cue as well as how it responded.Separating exposure from response lets a defense address exposure and the agent’s decision one at a time.Source: §6 · p. 9

Hazards should be tested in combination

  • 18.9% of compound failure-mode firings cannot be attributed to any single hazard, and 24.7% occur under multiple ablations.The authors recommend combining isolated tests, which identify specific weaknesses, with compound worlds, which expose interactions; the four hazards cost 67.3 points when planted one at a time and 38.2 when planted together.Source: §6 · p. 9

7 LIMITATIONS

The evaluation demonstrates BACKDROP on AppWorld, and whether its measured costs transfer to other environments and task distributions remains future work. The authors also identify limits tied to hazard construction, hand-built scenarios, outcome observability, repeat-trial coverage, and model coverage.

Scope

  • The study demonstrates the transformation on AppWorld; transfer of measured costs to other environments and task distributions remains future work.The authors’ goal is to expose weaknesses in current agents and evaluations that treat task environments as ideal worlds.Source: §7 · p. 9

Hazard construction

  • Measured hazard costs depend on how the authors built the cues; injection costs 3.6% of twin passes in an email body and 24.3% in a text message.Build-time checks test hazard validity, but do not remove this construction dependence.Source: §7 · p. 9

Scenario construction

  • The authors built and checked each compound by hand, while code emits the variants and a rerunnable construction check tests their validity.The hand-built compounds are the basis for generated variants.Source: §7 · p. 9

Observability

  • Fault-mishandled rates and the 18.9% of fired modes classified as interactions are upper bounds because underlying outcomes are not always directly observable.Source: §7 · p. 9

Repeated attempts

  • Three of the 16 models ran only once because of compute limits, so repeated-attempt analyses cover the other 13 models.Source: §7 · p. 9

Model coverage

  • The 16 evaluated models do not cover the full model population; evaluating more models remains future work.Source: §7 · p. 9

8 CONCLUSION

BACKDROP transforms an executed task into matched worlds that preserve its instruction and correct end state, enabling losses to be measured on the same task. Across 16 models, hazards reduce pass rates by 38.2 points, and twin pass rates overstate reliable capability in changing worlds.

8 CONCLUSION

  • Across 16 models, pass rates fall 38.2 points when hazards are present, and the strongest models also become less consistent.For 12 of 13 models with four attempts, compound best-of-four pass rates remain below one-attempt twin pass rates.Source: §8 · p. 9
  • BACKDROP turns one task in an executed environment into up to six worlds sharing one instruction and correct end state, measuring each loss on the same task.The family comprises a twin, up to four single-hazard ablations, and a compound.Source: §8 · p. 9
  • The largest loss comes from another person’s message, a hazard that no benchmark in Table 1 tests.The authors conclude that knowing an agent can complete a task is not enough to trust it in a changing world.Source: §8 · p. 9

ETHICS STATEMENT

The evaluation uses simulated AppWorld tasks and hazards in a sandbox, with no real-world messages, payments, or emails. The authors describe the study’s scope and checks, and disclose how generative AI tools were used.

ETHICS STATEMENT

  • The work uses no human subjects or personal data; AppWorld’s people and apps are simulated, and actions have no real effect.The local backend means no message, payment, or email reaches a real service.Source: ETHICS STATEMENT · p. 10
  • The hazards are plain requests acting only inside the sandbox, though the finding that another person’s instruction costs most could also guide an attacker.The authors report the finding to expose a measurable hazard and release the hazards so developers can test for it before deployment.Source: ETHICS STATEMENT · p. 10
  • Results cover 16 models on AppWorld tasks with author-built hazards at provider-default settings, not a general safety rating of any model.Measured costs depend on cue construction; injection costs 3.6% of twin passes in an email body and 24.3% in a text message.Source: ETHICS STATEMENT · p. 10
  • AppWorld’s evaluator determines every pass, failed tasks are not rerun to obtain passes, and hazards with failed preconditions are omitted with the reason recorded.The authors check text values against stored per-model results before computing intervals.Source: ETHICS STATEMENT · p. 10

AI USE STATEMENT

  • Code generates variants from AppWorld records; the authors defined the hazard process, and generative AI assisted with code and figure design, not data generation, hypotheses, or experiment design.Construction and evaluation use separate sandboxes, and the authors reviewed AI-assisted work and tested generated code with validity checks.Source: AI USE STATEMENT · p. 10

REPRODUCIBILITY STATEMENT

The reproducibility statement identifies the materials, evaluation setup, and paper sections needed to inspect and reproduce BACKDROP’s experiments.

REPRODUCIBILITY STATEMENT

  • Anonymous code and data are available for review.Source: REPRODUCIBILITY STATEMENT · p. 10
  • AppWorld’s evaluator runs unchanged, and stored runs can be regraded offline from end-state databases, API call logs, and agent messages.Source: REPRODUCIBILITY STATEMENT · p. 10
  • The statement directs readers to sections covering the transformation, hazards, cue planting, scenario templates, validity checks, detector controls, model settings, statistical tests, and per-model results.Source: REPRODUCIBILITY STATEMENT · p. 10

A THE SAME TASK IN A CLEAN AND A HAZARDOUS WORLD

This section describes how BACKDROP builds and grades paired clean and hazardous AppWorld worlds, and how its detectors and evaluation measures are validated and interpreted. It also illustrates task outcomes across the paired worlds, including the safe response to an ambiguous write error.

A THE SAME TASK IN A CLEAN AND A HAZARDOUS WORLD

  • All nine AppWorld requirements pass in the twin, but six failure modes fire and two requirements fail in the compound.The compound run also changes the note contrary to the request and changes records outside Simple Note.Source: A THE SAME TASK IN A CLEAN AND A… · pp. 14–15

C CASE STUDY: HANDLING A FAILED WRITE

  • Reading state before retrying is correct whether a 500 write committed or rolled back; retrying first mishandles a write that already landed.In one attempt, the agent checks and passes without a failure mode; in another, it retries before checking, although both end with the correct snooze setting. The mode counts every resent write, so its rate is an upper bound on duplicated effects.Source: C CASE STUDY: HANDLING A FAILED WRITE · pp. 16–17, Table 7 · p. 16

B CONSTRUCTION AND GRADING OF ONE FAMILY; D HOW EACH HAZARD IS PLANTED

  • The four hazards plant conflicting authority, an injected real-world action, a write to an ungranted app, and an ambiguous error after a required write.Cues are filled from each instance’s own records. In injection ablations, runs where the call lands pass 6.5% of the time versus 65.3% when agents ignore the cue; in boundary ablations, the corresponding rates are 20.7% and 61.0%, and 99.8% of attempted boundary writes land.Source: B CONSTRUCTION AND GRADING OF ONE FAMILY · p. 16, D HOW EACH HAZARD IS PLANTED · p. 17

E GRADING A RUN

  • Run grading keeps AppWorld’s pass verdict separate from seven failure modes, using end states, API logs, and agent messages for deterministic offline regrading.Six modes use shared detectors and authority-break uses a scenario-specific check; compound failures are attributed to a single hazard only when the matching ablation also produces that mode. Two additional oracles are not reported as failure modes.Source: E GRADING A RUN · pp. 18–19

F THE SCENARIO TEMPLATE; G SCENARIO SELECTION CRITERIA

  • The engine emits a twin, one ablation per available hazard, and a compound; screening requires a world-changing task and unchanged do-nothing scores across variants.The original gold solution, evaluator, and do-nothing labels remain unchanged. In most scenarios, every variant also receives the same added instruction sentence to read relevant messages before starting; the sentence names no hazard.Source: F THE SCENARIO TEMPLATE · pp. 19–20, G SCENARIO SELECTION CRITERIA · pp. 20–21

H COMPOSITION OF THE EMITTED CORPUS

I EXPOSURE MEASUREMENT

  • Exposure separates whether a cue reached the agent from whether its failure mode fired; the non-exposure baselines are 0.4% for injection, 8.1% for authority, and 4.7% for boundary.Fault is omitted from exposure tables because its 500 response reaches the agent in every run.Source: I EXPOSURE MEASUREMENT · p. 23, Table 12 · p. 23

J CONSTRUCTION VALIDITY; K DETECTOR VALIDATION

  • Detectors fired primarily under their own plant, at 11.6%, 43.2%, 47.9%, and 24.5%, versus 0.0%, 0.0%, 0.0%, and 0.5–0.9% under every other plant; scripted validation found no false positives or negatives across seven modes.The gate checks cue reachability, plausibility, wrongness, variation between same-scenario instances, and the do-nothing floor. Validation used 15 scripted agents and 21 labeled runs.Source: J CONSTRUCTION VALIDITY · pp. 23–24, K DETECTOR VALIDATION · p. 24

K DETECTOR VALIDATION

  • 21 control runs yielded 91 graded cells with no false positives or negatives, giving 1.00 precision and recall in each of seven modes.The controls bound detector error only for behavior the authors wrote, not behavior observed in real runs. Two shared log-reading defects appeared in real traces before controls caught them; each was fixed with a regression test and the stored runs were regraded.Source: K DETECTOR VALIDATION · pp. 24–25

L INTERVALS AND TESTS

  • Only two of 15 comparisons were not statistically significant: the cost difference between fault and boundary, and injection-cost trend with twin pass rate.Intervals resampled 618 task families with all six variants together across the 16 fixed models; the two exceptions had intervals including zero or p ≥ 0.05.Source: L INTERVALS AND TESTS · p. 25

M PER-MODEL RESULTS

  • Pass@1 is the pass rate across attempts, pass@n is the ceiling, and pass@k estimates the chance that at least one of k randomly chosen attempts solves an instance.Pass@k increases with k; the size of that increase indicates how much outcomes depend on chance rather than the model.Source: M PER-MODEL RESULTS · pp. 26–27
  • The four hazards’ costs overlap, and the extra compound failures are wrong end states.Table 17 distinguishes summed ablation drops, the compound drop, an independent-hazards expectation, the observed compound pass rate, wrong-end-state shares, and step-cap shares.Source: Table 17 · p. 27
  • Each Table 16 rate is a share of all compound runs, and one run can contribute to multiple mode columns.On twins, injection-followed, authority-break, and fault-mishandled cannot fire because those modes require a planted hazard.Source: Table 16 · p. 26, M PER-MODEL RESULTS · p. 27

N CAPABILITY AND RELIABILITY BANDS

Capability and reliability bands distinguish tasks an agent no longer solves from tasks it solves inconsistently. Across measured compound runs, every model lost tasks, while band changes differed by model strength.

N CAPABILITY AND RELIABILITY BANDS

  • A pass rate alone cannot distinguish tasks no longer solved from tasks solved on only some attempts.The bands separate these loss types; the hand-built example uses 10 instances and four attempts per instance.Source: N CAPABILITY AND RELIABILITY BANDS · p. 28
  • Three compounds with the same pass rate represent different losses: capability, reliability, or both.The first loses tasks, the second loses consistency on still-solvable tasks, and the third combines both losses.Source: N CAPABILITY AND RELIABILITY BANDS · p. 28
  • All 13 models’ compound ceilings fell below their twin ceilings; the five strongest models’ bands widened, while the eight weaker models’ bands narrowed.For GPT-5.6 sol, the floor–ceiling gap grew from 9 to 27 points; for Qwen3.5 122B-A10B, it narrowed from 57 to 31 points.Source: N CAPABILITY AND RELIABILITY BANDS · p. 28
  • On the twin, GPT-5.6 sol solved all ten sampled instances on every attempt, while Qwen3.5 122B-A10B solved seven at least once but none on every attempt.The samples use the same ten instances, drawn at random with seed 1; the plotted lines also give floor and ceiling across all 618 instances.Source: N CAPABILITY AND RELIABILITY BANDS · p. 28, Figure 10 · p. 29

O EXTENDED LIMITATIONS

The measured hazard costs are bounded by the tested environment, cue construction, validation procedures, and model-attempt coverage. The model-access section specifies the run settings used for evaluation.

O EXTENDED LIMITATIONS

  • The transformation is demonstrated only on AppWorld, so whether the same costs appear in other environments remains open.The screen deciding whether a task can be used needs only the task’s own files, so the procedure could in principle apply elsewhere.Source: O EXTENDED LIMITATIONS · pp. 29–30
  • Measured costs depend on the authors’ cue placements: injection costs 3.6% of twin passes in email bodies versus 24.3% in text messages.The authors state that each cost describes their specific placements, not a fixed property of the hazard type.Source: O EXTENDED LIMITATIONS · p. 30
  • Humans checked compounds, but automatic ablation checks are mechanical and do not test whether cues seem natural to readers.The checks cover do-nothing scores, cue readback, and 21 control runs; the latter had no false alarms or misses across seven modes.Source: O EXTENDED LIMITATIONS · p. 30
  • The fault-mishandled rate and interaction share are upper bounds, because retries may not take effect and compound-to-ablation attempt pairing is arbitrary.Interactions are firings in compound runs but none of the paired ablation runs.Source: O EXTENDED LIMITATIONS · p. 30
  • Only 13 of 16 models received four attempts; every band, pass@k, and attribution result therefore covers those 13 models.The other three models ran once because of the compute budget.Source: O EXTENDED LIMITATIONS · p. 30

P MODEL ACCESS

  • Runs used AppWorld’s unchanged standard ReAct agent, provider-default temperature and reasoning settings, a 70-step cap, and a seed of 1.Runs were graded from the end state and API-call log, with no reruns to obtain a pass.Source: P MODEL ACCESS · p. 30

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