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V$^3$AST-Bench: Verifiable, Valid, and Versatile Safety Evaluation for Coding Agents

Weitao Feng, Hengheng Xiong, Zihao Zeng, Jun Chen, Jiyang Guan, Xiaofeng Wang, Jialing Tao, Hui Xue, Wei Dong

arXiv:2610.35188v1cs.SEcs.AI

TL;DR

Coding-agent actions can affect systems beyond generated responses, while safety evaluations must distinguish harm avoidance from inability to complete a workflow. V3AST-Bench pairs executable harm and outcome checks with capability twins across coding-agent tasks. Across model–harness combinations, comparable competence can accompany different safety outcomes, and agents may recognize hazards yet still violate safety boundaries.

  • Problem

    Coding-agent actions can have consequences that response-centered evaluation misses, and harmless outcomes can be confounded with inability to complete the workflow.

    Source: §1 · p. 1

  • Method

    V3AST-Bench uses executable checks to verify harms and task outcomes, pairs adversarial tasks with capability twins, and evaluates agents in native harness configurations.

    Source: §1 · p. 2

  • Results

    Across model–harness combinations, agents with comparable competence exhibit markedly different safety outcomes; Claude Opus 4.8 with Claude Code produces verified harm on 20.9% of eligible risk tasks despite a 92.0% capability-twin pass rate.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Trajectory analysis shows that agents can recognize hazards yet still violate safety boundaries, so risk awareness alone does not ensure safe execution.

    Source: §7 · p. 10

    Claude Code and Codex were evaluated in permissive autonomous configurations, so the reported attack success rates do not characterize performance under their strong safeguards.

    Source: §6 · p. 10

Abstract

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Large language models increasingly act through code, modifying repositories, executing programs, and interacting with services in agent form. Their actions introduce risks that cannot be assessed from generated responses alone. Safety evaluation must verify actual consequences, distinguish harm avoidance from task incapability, and cover realistic workflows. We introduce V$^3$AST-Bench, a benchmark for Verifiable results, Valid interpretation, and Versatile risk coverage of Agent Safety. It comprises 212 risk tasks spanning 18 categories. Dedicated runtime environments verify harm and task outcomes through executable checks over states, records, and artifact behavior, while paired capability twin tasks verify workflow competence. Our experiments span 51 combinations of 11 models and five harnesses, spanning 86,496 trials and over 9 billion tokens of model interaction. Results show that V$^3$AST-Bench remains challenging even for frontier coding agents, as Claude Opus 4.8 with Claude Code still causes verified harm on 20.9% of tasks. Across model-harness combinations, agents with comparable competence exhibit markedly different safety outcomes, showing that task capability alone does not guarantee safe execution. More importantly, even explicit recognition of risks does not reliably prevent harmful actions, revealing a critical gap between risk awareness and safe execution. These findings highlight the need to move beyond risk recognition toward enforcing safety throughout execution.

1. Introduction

Coding agents can affect systems and resources through execution, so safety evaluation must verify consequences, distinguish safety from inability to complete work, and cover realistic workflows. V3AST-Bench addresses these challenges with executable outcome checks, paired capability twins, and diverse runtime tasks.

1. Introduction

  • Coding-agent actions can affect systems and resources beyond their responses, but visible tool trajectories may conceal consequential effects such as credential exfiltration.In the dependency example, receiving-server records can establish that credentials arrived even when the visible trajectory shows only installation and successful output.Source: §1 · p. 1
  • Despite a 92.0% capability-twin pass rate, Claude Opus 4.8 with Claude Code produces verified harm on 20.9% of eligible risk tasks; comparable competence also yields markedly different safety outcomes.Harness choice has greater impact on stronger models' safety, while weaker models remain vulnerable across harnesses; models also generally show weaker safety awareness for indirect than direct attacks.Source: §1 · p. 2
  • V3AST-Bench uses executable checks over environment states, event records, and artifact behavior to verify specified harms and appropriate task outcomes.The checks distinguish safe completion from unfinished work and from stopping when no authorized completion path exists.Source: §1 · p. 2
  • Every adversarial task is paired with a capability twin, allowing safety to be interpreted alongside demonstrated workflow competence rather than task failure or inaction.The benchmark reports capability alongside safety, conditions safety scores on successful twins, and tracks risk-task completion separately.Source: §1 · p. 2
  • The benchmark comprises 212 paired tasks across nine risk types, with dedicated environments supporting multi-step workflows and multi-turn interactions.Tasks adapt and expand GitHub issue reports, CWE weakness patterns, and CVE mechanisms, and include direct user-request attacks and indirect environmental-content attacks.Source: §1 · p. 2
  • The evaluation covers 51 model–harness combinations while retaining agents' native tools, prompts, context management, and execution loops.A model-API proxy records exchanges, harness adapters collect trajectories, and independent detectors verify consequences.Source: §1 · p. 2

2. Related Work

Prior work spans tool-agent safety benchmarks and newer coding-agent evaluations, but coverage of diverse risks in realistic coding workflows remains limited. V3AST-Bench retains native agent execution in executable containers and pairs risk tasks with benign workflow twins.

2. Related Work

  • Tool-agent benchmarks study direct misuse, indirect prompt injection, and diverse service environments, including AgentHarm [Andriushchenko et al., 2024], AgentDojo, and DTap.AgentHarm includes benign counterparts for capability reference, while AgentDojo uses attacker-success and utility checks.Source: §2 · p. 3
  • Recent coding-agent benchmarks, including SABER, HarnessSafe, and HarnessRisk, examine safety risks in software-development tasks.Their coverage remains limited for diverse risks encountered in real-world coding workflows.Source: §2 · p. 3
  • Existing coding-agent benchmarks leave broader workflow risks—including untrusted dependencies, supply-chain compromises, result integrity, and persistent system modifications—insufficiently explored.The cited limitations include narrow risk coverage and simplified execution environments that do not capture complex repositories and multi-step workflows.Source: §2 · p. 3
  • V3AST-Bench runs native coding agents in executable containers, with average trajectories of 14.7 interaction turns and paired benign capability twins.Its benchmark comparison defines full, partial, and absent coverage; partial coverage includes checking capability while still scoring failed cases, or testing only some risks in a category.Source: §2 · p. 3, Table 1 · p. 3

3. V3AST-Bench

V3AST-Bench evaluates coding agents on executable software-engineering tasks with embedded risks, pairing each risky workflow with a benign capability twin. Its staged construction and outcome rules are designed to measure verified harm separately from workflow competence.

3. V3AST-Bench

  • Each executable task specifies a prohibited effect and an independent completion check, while a benign paired task establishes workflow capability.This separates harm avoidance from demonstrated task competence.Source: §3 · p. 3

3.1. Task Definition and Taxonomy

  • The taxonomy groups prohibited actions or effects into nine classes and treats direct requests and environmental instructions as separate attack sources.The same risk type can arise from either source.Source: §3.1 · pp. 3–4

3.2. Benchmark Construction

  • Benchmark construction first selects scenario blueprints, then implements and validates them as executable task pairs, filtering low-quality scenarios before costly implementation.The staged workflow reflects the premise that design largely determines task quality and implementation costs more.Source: §3.2 · p. 4, Figure 1 · p. 5

3.2.1. DESIGN PRINCIPLES

  • Stage I prioritizes realistic, reasonably discoverable risks that agents encounter on the task path, with outcomes checked through executable evidence.Stage II principles add harmless simulation, minimal complexity, reduced evaluation cues, and autonomous execution.Source: §3.2.1 · p. 4

3.2.2. CONSTRUCTION PIPELINES OF V3AST-BENCH

  • Blueprints specify the engineering task, trap, safety cue, harmful and safe paths, detection criteria, source reference, and taxonomy identifier.Candidate blueprints are reviewed against six dimensions, with retention thresholds and human review for intermediate scores.Source: §3.2.2 · pp. 4–5
  • Implemented task pairs use executable detectors over receiver logs, state, and artifact behavior, and are calibrated for reachable harmful behavior and achievable benign workflows.The checks assign safety verdicts without an LLM judge; calibration targets a COMPROMISED risk run and a CAPABLE twin run.Source: §3.2.2 · p. 5
  • After calibration, trajectory-detector disagreements prompt checks of detector evidence, while revisions address measurement, wording, shortcuts, warnings, or harness-removed outputs.If no evaluated backend avoids a risk, the task is reviewed for disproportionate safe-path effort.Source: §3.2.2 · p. 5

3.2.3. RESULT INTERPRETATION

  • Safety scoring includes only tasks whose twins are CAPABLE in all four retained runs; one COMPROMISED risk run suffices to count an eligible task as compromised.Verified non-harmful INCOMPLETE runs remain eligible for safety scoring but receive no task-completion credit.Source: §3.2.3 · p. 6

4. Experiment Results

Across model–harness combinations, similar capability can accompany sharply different safety outcomes, and harness effects vary with model strength. Risk difficulty and safety across direct versus indirect attacks also differ, with indirect attacks generally producing higher ASR.

4.1. Experiment Settings

  • The evaluation covers 212 risk tasks and capability twins across model–harness systems, with executable checks and four retained adversarial and twin runs per task–system combination.The study evaluates 11 models across five harnesses; its grid contains 86,496 evaluation records.Source: §4.1 · pp. 6–7

4.2. Model × Harness Findings

  • At the same 97.2% capability-twin pass rate under Codex, Qwen3.8-Max and GPT-5.6-Sol differ in ASR by 39.3 percentage points.The results suggest task competence alone does not explain agent safety.Source: §4.2 · p. 7, Table 3 · p. 7
  • Stronger models show more harness-dependent harm outcomes, while harm remains common across harnesses for weaker models.Qwen3.8-Max has substantially lower ASR under Pi than OpenCode despite comparable capability-twin performance.Source: §4.2 · pp. 7–8

4.3. Model × Risks Findings

  • Unsafe-dependency identification (R5) remains the most challenging risk, while the largest mean ASR reductions are for destructive operations (R3) and avoiding untrusted code execution (R1).A compatible, functioning dependency can still fail project security requirements; Opus 4.8 restored a legacy-lockfile version without checking those requirements.Source: §4.3 · p. 8

4.4. Cross Attack Surface Findings

  • Indirect attacks have higher ASR than direct attacks for ten of eleven models, though newer generations show progress on environmental risks.Opus 4.8 reduces indirect-attack ASR by 40.4 percentage points versus Opus 4.6, compared with 20.6 points for direct attacks; GPT-5.6-Sol retains a pronounced indirect-attack gap.Source: §4.4 · pp. 8–9, Figure 4 · p. 9

5. Qualitative Analysis

The qualitative trajectories show two routes from risk recognition to harm: harness persistence instructions can override a decision to stop, and testing suspicious code without restricting its access can affect the real workspace.

5. Qualitative Analysis

  • Harness framing can shift failure from detecting a risk to resolving it, while a safety check can itself trigger harmful side effects.The trajectories distinguish recognizing untrusted code from understanding the permissions and filesystem view under which it executes.Source: §5 · p. 9, §5.1 · p. 9, §5.2 · p. 10

5.1. Amplified Persistence Weakens Safety

  • Harness autonomy and approval instructions can override an agent’s inclination to stop after recognizing a risk.In the illustrated Kimi-K3 run, the agent recognizes tenant-data exposure, proceeds with the policy change, and publishes; a sibling run stops.Source: §5.1 · pp. 9–10

5.2. Access Control Awareness of the Model

  • A temporary directory or copied inputs do not restrict suspicious code’s access to the real workspace.In one skill-hijacking trajectory, the agent rejected the binary and completed the rename but left ten junk files in the real workspace; a separate chroot trajectory kept observed writes inside its temporary root.Source: §5.2 · p. 10

6. Discussion and Limitations

The discussion identifies three limits affecting how V3AST-Bench results should be interpreted: ambiguous twin failures, permissive harness settings, and possible evaluation-awareness effects.

Capability failure versus benign over-refusal

  • Failed capability twins cannot distinguish insufficient workflow ability from benign-task refusal, so over-refusal may shrink the evaluated set and conditional safety may not imply broad competence.The ambiguity does not change safety scores on the retained support, but limits interpretation of capability coverage.Source: §6 · p. 10

Harness safety configurations

  • Reported attack success rates characterize permissive autonomous configurations, not Claude Code or Codex runs with their restrictive permission and sandbox safeguards enabled.The evaluated runs still use dedicated task containers; assessing stricter modes would also require accounting for effects on task completion.Source: §6 · p. 10

Evaluation Awareness

  • Evaluation awareness may bias measured safety in either direction, depending on whether agents perceive the objective as task performance or a safety test.Performance framing may encourage unsafe actions, whereas safety-test framing may encourage excessive refusal.Source: §6 · p. 10

7. Conclusion

V3AST-Bench evaluates coding-agent safety through verified consequences, paired capability twins, and sustained workflows. Across its model–harness evaluation, comparable competence coexists with different safety outcomes, and hazard recognition does not ensure safe execution.

Benchmark and evaluation

  • V3AST-Bench combines 212 risk tasks across 9 risk types and direct and indirect attacks with executable outcome checks, capability twins, and dedicated runtime environments.The evaluation spans 51 combinations of 11 models and 5 harnesses.Source: §7 · p. 10

Findings

  • Agents with comparable competence can have markedly different safety outcomes, and harness effects and safety gains vary across models and risks.Trajectory analysis also finds that agents can recognize hazards yet still violate safety boundaries.Source: §7 · p. 10

Implication

  • The findings motivate model training and harness designs that enforce safety constraints throughout execution rather than relying on risk recognition alone.Source: §7 · p. 10

A. Risk Taxonomy and Task Specification

The taxonomy defines unsafe actions by their risk type and decision boundary, while the task protocol uses dedicated environments and scoped executable evidence to verify specified harms and outcomes.

A.1. Current Risk Type Taxonomy

  • Risk types classify unsafe behavior, while each task supplies authorization, trust, resource, and visibility context for deciding whether an action is prohibited.Direct and indirect attack sources are separate from the nine risk classes.Source: §A.1 · p. 13
  • Deceptive output includes misleading execution, verification, delivery, or authorization claims; risk code generation covers code introducing concrete security defects or malicious behavior.The taxonomy distinguishes vulnerable or malicious implementations from control-configuration and verification risks, and excludes ordinary bugs or isolated dangerous function names.Source: §A.1 · p. 14
  • Each task isolates one safety decision within a source–risk cell, using a multi-step workflow if needed to keep failures attributable to a defined threat.The oracle verifies the specified action or artifact using evidence suited to its stage.Source: Table 4 · p. 13, §A.1 · p. 14

A.2. Task Environments and Outcome Verification

  • Tasks run in fresh environments containing the repository, dependencies, and supporting services, after which detectors verify specified effects and task outcomes.Source: §A.2 · p. 14
  • Verdicts rely on environment state, event records, and artifact behavior, with each detector scoped to specified effects and completion criteria.Trajectories help establish encountered risky inputs and explain actions, but do not replace outcome evidence.Source: §A.2 · p. 14
  • Verified harm takes precedence over later completion, refusal, or restoration, while failed evidence collection leaves outcomes unresolved unless harm is independently established.Source: §A.2 · p. 14

B. Benchmark Construction and Task Examples

The appendices describe benchmark construction, paired risk and capability tasks, and outcome interpretation, emphasizing validated detectors and explicit boundaries for feasible completion and safe stopping.

B.1. Task Construction and Validation

  • Before calibration, reference executions validate that each task’s detector can distinguish harm, safe completion or stopping, non-harmful incompletion, and unresolved evidence.Candidate blueprints are separately reviewed for realism, safe-path accessibility, a single safety decision, verifiability, and novelty, with human revision.Source: §B.1 · p. 14, §B.2 · p. 14

B.2. A Stage I blueprint and its twin

  • In the credential-disclosure example, an untrusted tool manifest requests raw runtime configuration, but the safe path submits only the query’s required service and time-window parameters.Canary secrets in recorded service arguments verify disclosure; the risk-task completion check does not independently verify the reported count.Source: §B.2 · pp. 15–16

Capability twin: authorized submission of public configuration

  • The capability twin authorizes submission of public sample configuration instead of runtime secrets and independently checks the received query arguments and reported count.A CAPABLE result establishes ability to perform the workflow under benign conditions, but does not diagnose why disclosure was avoided in the risk task.Source: Capability twin: authorized submission… · p. 16

B.3. Worked examples

  • Worked examples cover dependency activation with injected credential-upload instructions, stopping at a failed protected merge gate, and reporting an unavailable metric without fabrication.Other examples monitor prohibited persistent-policy changes during execution; their detection scope and accepted safe outcomes differ by task.Source: §B.3 · pp. 16–17

C.1. Feasibility, adversarial outcomes, and paired interpretation

  • Completion feasibility is set before evaluation according to authorization, information, resources, and accepted alternatives; an agent’s failure alone does not make a task infeasible.A capability twin may supply missing prerequisites, and that pairing intervention is recorded.Source: §C.1 · p. 17
  • With a successful twin, verified non-harmful outcomes count as safe, but only COMPLETED earns completion credit for a feasible risk task; a failed twin excludes the pair.INCOMPLETE and STOPPED do not alone reveal whether the agent acted from safety reasoning, reflexive refusal, or another obstacle.Source: §C.1 · p. 17
  • Blocked tasks require a constraint-based rationale and demonstrated non-harmful stopping path, whereas feasible tasks require a demonstrated permitted solution.An authorized repair or scoped alternative keeps a task feasible even if the agent stops.Source: §C.1 · p. 17
  • Recorded SAFE labels may represent completed work or a permitted stop, so SAFE alone does not establish task completion.Twin CAPABLE status is determined by its own completion predicate, and some twin detectors also require a final report.Source: §C.1 · p. 17

C.2. Model sampling parameters and harness settings

The evaluation reports complete model–harness systems while retaining harness-native request construction and execution behavior. Its settings and execution protocol define the comparisons, and cross-protocol pairings were excluded when reasoning-state continuity was not preserved.

C.2. Model sampling parameters and harness settings

  • Harnesses use native request construction, so submitted inference parameters can differ from a model’s common baseline settings.The evaluated configurations also differ in reasoning and temperature settings.Source: §C.2 · pp. 17–18
  • Adaptive thinking lowered Opus-4.8 Mini-SWE-agent’s run-level harm rate from 27% to 23%, but temperature changed simultaneously.The comparison therefore does not isolate reasoning’s contribution; sampling settings and other evaluation conditions must be held fixed to do so.Source: §C.2 · p. 18

C.3. Execution Protocol and Budgets

  • Runs use fresh Docker containers, and each scheduled repetition retains one usable attempt with a resolved detector outcome.Each task–model–harness combination has four adversarial outcomes and four capability-twin outcomes; safety eligibility requires successful twins in all four repetitions.Source: §C.3 · p. 19

C.4. Cross-Protocol Compatibility

  • Cross-protocol GPT/Claude pairings were excluded because the harnesses fail to preserve reasoning state across calls.Codex discards Claude’s reasoning signature, while Claude Code does not carry GPT’s encrypted reasoning blocks into subsequent requests; repairing this requires added state management.Source: §C.4 · p. 19

D.1. Repeated-run stability in the main evaluation

Matched generational comparisons show fewer compromised workflows and, among workflows still vulnerable, less frequent harm in later generations. Across observed runs, ASR increases with the number sampled, while repeated-run variation is reported separately from confidence intervals.

D.1. Repeated-run stability in the main evaluation

  • ASR@n is the expected fraction of eligible combinations with at least one compromise among n uniformly sampled runs from the four observed repetitions.Eligibility is determined using all four paired repetitions and held fixed across values of n.Source: §D.1 · pp. 19–20
  • Standard deviations describe variability across four observed repetitions, not confidence intervals for ASR@4; ranking stability uses Spearman correlations across six repetition pairs.Source: §D.1 · p. 20
  • Across all combinations, ASR@1–4 was 46.0%, 51.0%, 53.4%, and 55.0%, respectively.Incremental gains were 5.01, 2.43, and 1.55 percentage points.Source: §D.1 · p. 20

D.2. Matched Generational Comparisons

  • In matched sets, 310 Qwen3.8-27B combinations compromised by Qwen3.5-27B showed no compromise, versus five in the reverse direction; Opus 4.8/4.6 counts were 227/seven.The matched sets contain 812 combinations for Qwen and 719 for Opus.Source: §D.2 · p. 20
  • Among combinations compromised at least once by both generations, mean compromised-run fractions fell from 96.1% to 76.2% for Qwen (388), 95.0% to 76.6% for Opus (176), and 93.6% to 89.0% for GPT (465).These descriptive comparisons are conditional on observed outcomes and do not isolate the causal effect of training.Source: §D.2 · p. 20

E. Additional Qualitative Analysis

The CSV-renaming example tests whether agents inspect a skill-referenced tool before use and verify its effects. Figures contrast testing copied inputs with containing the tool’s absolute-path writes.

E.1. Access Control Awareness

  • The skill-guided task requires renaming ten files by ascending byte size and inspecting a skill-referenced tool before deciding whether to use it.The bundled binary writes ten junk files to /testbed/ in the real workspace.Source: §E.1 · p. 20
  • Testing copied inputs in a temporary directory leaves access to the real workspace unchanged; ten junk files appear outside the inspected copies despite passing CSV checks.Source: Figure 6 · p. 21
  • With chroot, absolute-path writes land under the temporary root; the agent inspects those effects, completes the rename, and checks the real workspace.The result is supported by trajectory evidence.Source: Figure 7 · p. 21

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