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ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents

Ido Levy, Ben Wiesel, Sami Marreed, Alon Oved, Avi Yaeli, Nir Mashkif, Segev Shlomov

arXiv:2410.06703v7cs.AI

TL;DR

Existing web-agent benchmarks measure whether tasks finish but not whether agents follow safety and trustworthiness policies, limiting evidence for enterprise use. ST-WebAgentBench adds policy-paired enterprise tasks, multidimensional ST evaluation, and policy-aware metrics; across evaluated agents, compliant completion is substantially below nominal completion.

  • Problem

    Existing web-agent benchmarks primarily evaluate task completion and overlook safety and adherence to organizational policies.

    Source: §1 · p. 1

  • Method

    ST-WEBAGENTBENCH evaluates enterprise web tasks with explicit policy templates across six ST dimensions using CuP, pCuP, and Risk Ratio.

    Source: §1 · p. 2, §3.2 · p. 4

  • Results

    Fewer than two-thirds of nominal task successes survive the policy filter, with CuP declining from 18.2% under one active policy to 7.1% under five or more.

    Source: §4.2 · p. 9, §5 · p. 10

  • Takeaways & Limitations

    Completion rate alone is insufficient for judging enterprise readiness; policy-robust optimization must also be evaluated.

    Source: §1 · p. 2

    The evaluation covers three open agents sharing a GPT-4o backbone and 375 English-language tasks across three applications.

    Source: §5 · p. 10

Abstract

from arXiv · show

Autonomous web agents solve complex browsing tasks, yet existing benchmarks measure only whether an agent finishes a task, ignoring whether it does so safely or in a way enterprises can trust. To integrate these agents into critical workflows, safety and trustworthiness (ST) are prerequisite conditions for adoption. We introduce \textbf{\textsc{ST-WebAgentBench}}, a configurable and easily extensible suite for evaluating web agent ST across realistic enterprise scenarios. Each of its 222 tasks is paired with ST policies, concise rules that encode constraints, and is scored along six orthogonal dimensions (e.g., user consent, robustness). Beyond raw task success, we propose the \textit{Completion Under Policy} (\textit{CuP}) metric, which credits only completions that respect all applicable policies, and the \textit{Risk Ratio}, which quantifies ST breaches across dimensions. Evaluating three open state-of-the-art agents reveals that their average CuP is less than two-thirds of their nominal completion rate, exposing critical safety gaps. By releasing code, evaluation templates, and a policy-authoring interface, \href{https://sites.google.com/view/st-webagentbench/home}{\textsc{ST-WebAgentBench}} provides an actionable first step toward deploying trustworthy web agents at scale.

1 INTRODUCTION

Existing web-agent benchmarks emphasize task completion while omitting safety and policy compliance, despite deployment risks from unsafe actions. ST-WEBAGENTBENCH addresses this gap with policy-aware evaluation, revealing substantially lower compliant completion than raw success.

1 INTRODUCTION

  • Existing benchmarks primarily measure task completion and do not test whether agents respect organizational or user policies.Table 1 is presented as confirming this evaluation gap.Source: §1 · p. 1, Table 1 · p. 2
  • ST-WEBAGENTBENCH pairs enterprise web tasks with explicit policies and evaluates safety and trustworthiness across six dimensions.It includes human-in-the-loop hooks and reusable policy templates for extensible evaluation.Source: §1 · p. 2
  • CuP credits only fully completed tasks satisfying every policy, while pCuP captures partial compliant progress and Risk Ratio quantifies violations by dimension.Together, these metrics assess effectiveness alongside compliance rather than treating completion rate as sufficient.Source: §1 · p. 2
  • 24.3% raw CR falls to 15.0% CuP across three open SOTA agents, a roughly 38% relative drop in policy-compliant completion.Only about 62% of nominal completions satisfy all policy constraints.Source: §1 · p. 2
  • The benchmark studies policy-load scalability, including how increasing active policies affects compliance and risk.Its empirical contributions include dimension analysis and stress tests under increasing policy loads.Source: §1 · pp. 2–3

2 RELATED WORK

Web-agent benchmarks have progressed from static navigation datasets to dynamic online environments, but they largely evaluate automation rather than policy compliance. Related safety efforts address hazards, attacks, or dialogue harms, whereas ST-WEBAGENTBENCH evaluates multidimensional policy breaches during realistic web workflows.

2 RELATED WORK

  • Existing web-agent benchmarks increasingly model dynamic interaction but primarily score task completion and intermediate navigation goals.Examples include Mind2Web, WebVoyager, WebArena, Visual-WebArena, WorkArena, and WebCanvas.Source: §2 · p. 3
  • Existing controllers and policy-based systems attempt to enforce safety, but ST-WEBAGENTBENCH instead provides an application-agnostic evaluation layer that scores every policy violation across agents.This supports direct safety comparisons rather than relying only on control mechanisms.Source: §2 · p. 3
  • Safety-oriented benchmarks and taxonomies cover hazardous instructions, tool failures, prompt attacks, post-hoc logs, or dialogue harms without modeling policy-constrained web workflows.They also do not trace multidimensional trustworthiness breaches to concrete UI actions.Source: §2 · p. 3

3 ST-WEBAGENTBENCH: A SAFETY AND TRUSTWORTHINESS BENCHMARK

ST-WEBAGENTBENCH evaluates enterprise web agents by combining hierarchical policies, six safety–trustworthiness dimensions, policy-aware metrics, and modular task templates across diverse applications and modalities.

3.1 POLICY HIERARCHY IN ENTERPRISE WEB AGENTS

  • The benchmark reconciles organizational, user, and task policies in precedence order, with violations mapped to safety, trustworthiness, or task-success failures.Its hierarchy is Porg ≻ Puser ≻ Ptask, and new tasks can instantiate reusable policy templates through JSON entries.Source: §3.1 · p. 4

3.2 SAFE AND TRUSTWORTHY DIMENSIONS

  • Six orthogonal dimensions—user consent, boundary and scope, strict execution, hierarchy adherence, robustness and security, and error handling—form the benchmark’s evaluation backbone.They were derived from incident reviews, established frameworks, and stakeholder input, and together explain 95 % of cited incident causes.Source: §3.2 · pp. 4–5

3.3 EVALUATION METRICS

  • Completion rate and partial completion rate measure raw capability, whereas CuP and pCuP count successful work only when policy violations are absent.RiskRatio reports task-normalized residual violations by dimension, while all-pass@k measures the fraction of tasks succeeding in every repeated run.Source: §3.3 · p. 5

3.4 BENCHMARK DESIGN AND IMPLEMENTATION

  • The benchmark contains 375 policy-enriched tasks across GitLab, ShoppingAdmin, and SuiteCRM, totaling 3,057 policy instances across six dimensions.It uses BrowserGym extensions and modular policy evaluators, with context-dependent templates injected where they provide distinctive diagnostic signal.Source: §3.4 · p. 6

3.5 MODALITY CHALLENGE

  • The 80-task Modality Challenge isolates visual and accessibility-tree information to measure each modality’s marginal contribution.Vision-Advantage tasks require pixel-level signals, whereas DOM-Advantage tasks require semantic HTML and ARIA information absent from screenshots.Source: §3.5 · p. 6

3.6 EVALUATION TEMPLATES

  • Reusable policy templates and shared evaluation functions encode constraints and validate agent trajectories against ground-truth compliance criteria.The evaluators check actions, sequences, URLs, user messages, action counts, HTML, sensitive-data handling, and hallucinated inputs within BrowserGym’s multimodal environment.Source: §3.6 · pp. 6–8

4 EXPERIMENTS

The experiments evaluate three public web agents on a 375-task, policy-enriched suite and compare raw completion with policy-compliant performance. Results show that safety and trustworthiness constraints expose substantial gaps, especially as policy complexity increases.

4.1 EXPERIMENTAL SETUP

  • The benchmark evaluates AWM, WorkArena-Legacy, and WebVoyager using pass@3 across GitLab, ShoppingAdmin, and SuiteCRM, with each agent run once over 375 tasks.Policies were supplied through a POLICY_CONTEXT block appended to every observation.Source: §4.1 · p. 8

4.2 RESULTS

  • AWM reaches 46.9% PCR but only 20% CuP, while WorkArena-Legacy reaches 26% CR and 15% CuP, and WebVoyager reaches 12.8% CR and 10.3% CuP.The strict all-pass@3 rate remains low: 5.0% for AWM, 3.0% for WorkArena-Legacy, and 2.0% for WebVoyager.Source: §4.2 · p. 8
  • User-consent and strict-execution templates dominate the risk distribution, whereas boundary-scope rules are rarely triggered because agents seldom reach them in long workflows.These patterns expose weaknesses in permission handling and hallucination control that raw success metrics can mask.Source: §4.2 · pp. 8–9
  • With all dimensions enforced, completion and CuP correlate only modestly at ρ_full = 0.48, while removing any dimension increases the correlation.Consent and strict-execution removals produce the largest increases, +0.13 and +0.15 respectively; boundary and error-handling produce only +0.02–0.03.Source: §4.2 · p. 9, Table 3 · p. 9
  • CuP falls from 18.2% with one active policy to 7.1% with more than five, while policy load correlates negatively with compliance at ρ = −0.71.Raw completion is nearly flat across bins (ρ = −0.14), and per-task risk ratio grows roughly linearly by 0.11 ± 0.02 per added policy.Source: §4.2 · p. 9
  • The results indicate that current agents trade off task performance against strict policy compliance and are not yet ready for high-stakes enterprise deployment.The authors argue that future agents should prioritize policy compliance alongside task completion.Source: §4.2 · p. 10

5 CONCLUSION

ST-WebAgentBench unifies web-agent task success with explicit safety and trustworthiness constraints through policy-enriched tasks, six dimensions, and policy-aware metrics. Its evaluations show that nominal completion degrades under policy filtering and increasing constraint loads, motivating simultaneous optimization for capability and compliance.

5 CONCLUSION

  • ST-WebAgentBench pairs 375 enterprise-style tasks with 3,057 policy instances across six ST dimensions and scores agents using CuP, pCuP, and Risk Ratio.The benchmark is designed as an extensible, policy-aware evaluation framework.Source: §5 · p. 10
  • Fewer than two-thirds of nominal successes survive the policy filter, with 70% of violations concentrated in user-consent and strict-execution dimensions.The conclusion presents this as evidence that raw completion is an incomplete objective for web-agent evaluation.Source: §5 · p. 10
  • CuP falls from 18.2% to 7.1% as the number of active policies rises beyond five, revealing fragility under increasing constraint loads.The reported trend supports treating policy compliance as a first-class evaluation target.Source: §5 · p. 10
  • The benchmark supplies template-level failure diagnostics and points to continuous policy context, explicit consent and escalation actions, and policy validation as design principles.Appendix I and Fig. 17 outline a lightweight controller architecture that operationalizes these insights.Source: §5 · p. 10
  • The study evaluates only three open agents sharing a gpt-4o backbone and covers 375 English-language tasks across three applications.It is framed as a reusable foundation rather than an exhaustive commercial leaderboard or deployment gatekeeper.Source: §5 · p. 10

REPLICABILITY AND ETHICS

The paper emphasizes reproducible, controlled evaluation and situates its benchmark within the rapid growth of web-agent systems. It reports open artifacts while noting that existing WebArena agents remain limited even without trustworthiness policies.

REPLICABILITY AND ETHICS

  • The framework, codebase, resources, datasets, metrics, and experimental setups are intended to be openly accessible for replication.Documentation and environment scripts include the ST-WebAgentBench integration with BrowserGym.Source: REPLICABILITY AND ETHICS · p. 16
  • Evaluations should run in isolated, controlled environments to prevent unintended harm during agent testing.The paper also states that its datasets exclude sensitive or personally identifiable information and follow privacy and consent requirements.Source: REPLICABILITY AND ETHICS · p. 16

A WEB AGENTS

  • A WebArena leaderboard snapshot records around 20 agents appearing in one year, while frontier-model agents still achieve relatively low success rates without trustworthiness policies.The table is sorted by release date and provides context for the pace of agent development.Source: Table 4 · p. 16

B DERIVING THE SIX SAFETY–TRUST DIMENSIONS

The six safety–trust dimensions were derived from incident reviews, established frameworks, and expert input, then consolidated to reduce semantic overlap. The resulting dimensions cover most cited incident causes but remain a validated starting point subject to cross-industry expansion.

B DERIVING THE SIX SAFETY–TRUST DIMENSIONS

  • A desk review of 36 incident summaries and AI-safety taxonomies produced ten candidate dimensions for expert assessment.The candidate list included User-Consent, Boundary, Strict Execution, Hierarchy, Robustness, Error-Handling, Explainability, Latency Sensitivity, Cost Awareness, and Accessibility.Source: B DERIVING THE SIX SAFETY–TRUST… · p. 17
  • Ten senior stakeholders from automation, security, and governance roles participated in the expert panel and answered a common set of questions about unacceptable behaviors and automation failures.The survey was anonymous and voluntary, with participants able to skip questions or quit.Source: B DERIVING THE SIX SAFETY–TRUST… · p. 17
  • The synthesis merged overlapping labels, such as Sensitive-Information Leakage and Jailbreaking into Robustness & Security, while preserving orthogonality where possible.The final six dimensions were selected from categories that were theoretically grounded and independently validated by expert consensus.Source: B DERIVING THE SIX SAFETY–TRUST… · p. 17
  • The final dimensions are User-Consent, Boundary, Strict Execution, Hierarchy, Robustness, and Error-Handling.Expert mention rates were 10/10, 9/10, 8/10, 7/10, 6/10, and 6/10 respectively.Source: B DERIVING THE SIX SAFETY–TRUST… · p. 17
  • The six dimensions jointly covered 95% of cited incident causes.This coverage supports their use as a starting point for benchmark policy design.Source: B DERIVING THE SIX SAFETY–TRUST… · p. 17
  • The expert panel shared a common organizational context, which may introduce bias and motivates broader cross-industry participation.The authors characterize the dimensions as a validated starting point rather than a final taxonomy.Source: B DERIVING THE SIX SAFETY–TRUST… · p. 17

C CUP SCALABILITY UNDER INCREASING POLICY LOAD

As policy load increases, raw completion remains nearly unchanged while policy-compliant completion declines and risk rises. The section also introduces policy-constrained task schemas and consent examples.

C CUP SCALABILITY UNDER INCREASING POLICY LOAD

  • 18.2% CuP with one policy falls to 7.1% with more than five, while mean Risk Ratio rises roughly 0.11 ± 0.02 per added policy.Raw Completion Rate remains effectively constant (Spearman ρ = −0.14), whereas CuP correlates negatively with policy load (ρ = −0.71, p < 0.001).Source: C CUP SCALABILITY UNDER INCREASING… · p. 18, Figure 3 · p. 18

D SAFETY DIMENSION TASK EXAMPLES

D.1 SCHEMA

  • Each task policy is represented as a JSON schema that specifies the agent’s goal and mandatory rule, with hierarchy information preserved.The schema supports policy-constrained tasks across the benchmark’s safety dimensions.Source: §D.1 · p. 18, Table 5 · p. 19

D.3 BOUNDARY AND SCOPE LIMITATION

The benchmark instantiates safety dimensions through task configurations and injects policy constraints into agents without requiring architectural changes. Its integration strategy uses early, dynamic, and architecture-agnostic policy loading.

D.3 BOUNDARY AND SCOPE LIMITATION

  • The task examples operationalize boundary, strict-execution, robustness, and error-handling dimensions through dedicated configurations and illustrative agent behaviors.Examples include hallucinated inputs, policy contradictions, jailbreaking, and popup or missing-parameter handling.Source: Table 7 · p. 20, Table 8 · p. 20, Table 9 · p. 21

D.4 STRICT TASK EXECUTION

  • Figures illustrate both compliant and violating behaviors, including deletion of a database user, hallucinated account entries, hierarchy adherence, and jailbreak resistance.The examples make the safety dimensions concrete through observed agent interactions.Source: Figure 5 · p. 21, Figure 6 · p. 22, Figure 7 · p. 23

E INJECTING POLICY_CONTEXT INTO WEB AGENTS

E.2 IMPLEMENTATION DETAILS

Implementation builds a task-specific policy context by loading, formatting, and concatenating active policies with hierarchy and verification guidance. The template also standardizes confirmation, conflict resolution, and safe handling of violations.

E.2 IMPLEMENTATION DETAILS

  • For each task, active policies are retrieved, formatted into actionable constraints, inserted into {POLICIES}, and concatenated with the system prompt.This five-step process preserves policy hierarchy and dimension categories throughout execution.Source: §E.2 · p. 25

F FULL POLICY_CONTEXT TEMPLATE

  • The template encodes the precedence rule Porg > Puser > Ptask, making organizational policies override user preferences and task instructions.Conflict handling requires rejecting actions that violate organizational policy and clarifying lower-level conflicts when appropriate.Source: §E.3 · p. 25, F FULL POLICY_CONTEXT TEMPLATE · pp. 26–27

E.3 POLICY CONTEXT TEMPLATE

  • The six dimensions provide actionable checks for consent, boundaries, strict execution, hierarchy, security, and error handling before actions occur.The template turns policy compliance into a verification checklist rather than an abstract instruction.Source: §E.3 · pp. 25–26, F FULL POLICY_CONTEXT TEMPLATE · pp. 27–28
  • When explicit approval is required, the human-in-the-loop protocol standardizes a confirmation request that names the action and policy reason.The protocol is intended to maintain consistent safety checkpoints across agent architectures.Source: §E.3 · pp. 25–26, F FULL POLICY_CONTEXT TEMPLATE · p. 28

E.4 POLICY FORMATTING AND RUNTIME INTEGRATION

  • Runtime formatting preserves each policy’s level, constraint, and expected behavior while keeping the resulting instructions readable and machine-interpretable.A user-consent policy is shown as a formatted instruction to ask permission before clicking a creation button.Source: §E.4 · p. 26

G BENCHMARK DESIGN

The benchmark models enterprise web-agent risks from adversarial or misleading environments, evaluates policies with task-specific evaluators, and extends the action space for human confirmation. Examples show unintended navigation and hallucinated inputs as concrete violations.

G.1 THREAT MODEL

  • The threat model treats web interfaces and their data as partially trusted, stressing agents with prompt injection, hallucinated inputs, policy conflicts, and unsafe escalation.The benchmark pairs tasks with explicit policies and targeted conflicting or adversarial UI content, then scores policy-respecting completion.Source: §G.1 · pp. 28–29
  • The benchmark excludes network attacks, model-weight threats, supply-chain compromise, and multi-agent collusion, framing itself as a foundation rather than a comprehensive security gate.These threat vectors require different evaluation infrastructure.Source: §G.1 · p. 29

G.2 POLICIES AND EVALUATORS

  • Each task contains one or more policies, and each policy includes evaluators that test whether the agent violated its constraints.The benchmark provides example task-policy syntax and evaluation operators.Source: §G.2 · p. 29, Table 16 · p. 30, Table 17 · p. 31
  • The evaluator implementation checks trajectories for required user messages and returns violation reports when confirmation behavior is missing or incomplete.The code examples distinguish cases where neither action, only the first action, or both actions ask the user.Source: §G.2 · p. 33

G.3 ACTION SPACE

  • The action space extends prior web-agent actions with a human-in-the-loop tool that supports confirmation through an auto-confirming user proxy.This enables explicit confirmation actions within benchmark trajectories.Source: §G.3 · p. 35, Table 19 · p. 35

H.1 AGENT POLICY VIOLATION

  • Illustrative failures include navigating from issue creation to unwanted repository creation and hallucinating irrelevant account fields during account setup.These examples demonstrate unintended operations and fabricated inputs in realistic UI workflows.Source: §H.1 · p. 36, Figure 10 · p. 36, Figure 11 · p. 37

H.2 QUALITATIVE ANALYSIS OF DOMINANT RISK DIMENSIONS

Across the evaluated agents, User-Consent and Strict-Execution violations dominate risk, with failures involving bypassed confirmation, fabricated inputs, unauthorized deletion, and modality-specific perception limits.

H.2 QUALITATIVE ANALYSIS OF DOMINANT RISK DIMENSIONS

  • User-Consent and Strict-Execution violations dominate the risk distribution across all three agents.Together, these two dimensions account for 70% of total violations.Source: §H.2 · p. 37, §H.4.1 · p. 41

H.2.1 USER-CONSENT VIOLATIONS

  • Agents frequently bypass required confirmation before irreversible actions, treating task instructions as superseding user-level consent policies.This violates the hierarchy in which organizational consent rules override task goals; AWM exhibits a reported risk ratio of 0.44%.Source: §H.2.1 · p. 37

H.3 FUZZY MATCHING

  • The fuzzy-matching evaluation is deterministic, using exact matching except for user-message evaluators that apply RapidFuzz against policy templates.This preserves robustness to minor phrasing variation without using an LLM as judge.Source: §H.3 · p. 37

H.4 MODALITY CHALLENGE: TASK EXAMPLES

  • Vision-Advantage tasks encode answers in visual properties absent from the AXTree, including CSS colors, rotated layouts, and canvas chart pixels.DOM-only agents cannot access these signals, while vision-capable agents can read them from the rendered page.Source: §H.4 · p. 38, Figure 12 · p. 38, Figure 13 · p. 38
  • DOM-Advantage tasks place answers in accessibility-tree content invisible or unavailable in the screenshot, such as offscreen rows and hidden data attributes.DOM-capable agents can access these values directly, whereas vision-only agents must scroll or cannot distinguish them.Source: §H.4 · p. 38, Figure 15 · p. 39, Figure 16 · p. 40

H.4.1 STRICT-EXECUTION VIOLATIONS

  • Strict-Execution failures include fabricating unspecified account fields and deleting records despite explicit organizational prohibitions.These examples show both unauthorized modification and violation of a no-deletion constraint.Source: §H.4.1 · p. 41
  • Agents overgeneralize missing information and lose track of constraints during multi-step workflows instead of requesting clarification through human oversight.The reported failure pattern is fabrication when required parameters are missing.Source: §H.4.1 · p. 41

I FUTURE POLICY-AWARE ARCHITECTURE

The proposed architecture makes policy enforcement a centralized control layer around a base agent. It uses active policy context to filter actions and trigger consent or escalation when required.

I FUTURE POLICY-AWARE ARCHITECTURE

  • Policy-aware agents should maintain full policy context at every step and expose ask, confirm, escalate, and defer behaviors as explicit tool actions.These design choices are intended to reduce long-horizon drift, unsafe guessing, and consent-template violations.Source: I FUTURE POLICY-AWARE ARCHITECTURE · p. 41
  • A dedicated policy controller consumes active POLICY_CONTEXT, filters or amends candidate actions, and triggers consent or escalation actions when required.The controller centralizes organizational, user, and task-level constraint enforcement while leaving planning and perception to the base agent.Source: I FUTURE POLICY-AWARE ARCHITECTURE · p. 41, Figure 17 · p. 42
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