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OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution

Yunhao Chen, Xin Wang, Yixu Wang, Yi Liu, Jie Li, Yan Teng, Xingjun Ma, Xia Hu, Yu-Gang Jiang

arXiv:2608.00677v1cs.CL

TL;DR

Persistent agent-safety benchmarks provide limited coverage of failures arising from evolving state over long workflows. OpenART evaluates such risks through controlled environment evolution with EMHA, achieving 85.0% pooled Strict ASR and greater gains over instruction-only evolution on complex scenarios.

  • Problem

    Existing agent-safety benchmarks mainly use static or resettable environments and short workflows, limiting coverage of persistent state manipulation and delayed safety failures.

  • Method

    OpenART evaluates long-horizon executable scenarios by evolving target-visible environments while keeping task objectives and safety contracts fixed, using the black-box EMHA policy.

  • Results

    85.0% pooled Strict ASR is achieved across 75 agent–model configurations, with environment-evolution gains increasing from 1.8–2.7% to 17.2–17.6% as scenario complexity grows.

  • Takeaways & Limitations

    Environment evolution increasingly exposes safety failures in complex scenarios, while agent implementation explains safety variation beyond the foundation model.

  • Takeaways & Limitations

    The evaluation keeps task objectives and evaluators fixed and uses frozen target and attacker models without parameter updates.

Abstract

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AI agents operate in persistent environments where early state changes can influence decisions far into the future. Unlike conventional language-model interactions, agent behavior is mediated through a shared state that is repeatedly modified and reused across long-horizon workflows. Current safety benchmarks often fail to capture these cumulative risks because they focus on short, static tasks. To address these limitations, we introduce OpenART, an open-ended arena for scalable agent red teaming through environment evolution. OpenART provides over 10,000 validated stateful scenarios across 50 domains, drawing from a pool of more than 500,000 tools and skills. These tasks require a median of 97 tool calls and enable unified evaluation across 75 different agent-model configurations. To systematically explore these evolving attack surfaces, we propose the Evolutionary Markov Hypergraph Attack (EMHA). EMHA is a black-box policy that performs feedback-driven environment evolution by coordinating authorized state transitions without requiring parameter updates. Throughout the evaluation, task objectives remain fixed while only the environment state changes. Across all configurations, EMHA achieves a pooled Attack Success Rate (ASR) of 85.0%. Its advantage over instruction-only evolution increases from approximately 2% on simple environments to over 17% on the most complex ones, demonstrating that environment evolution increasingly exposes safety failures as task complexity grows. Furthermore, our analysis shows that the specific runtime implementation of an agent explains a significant portion of safety variation beyond the underlying model's capabilities. These results establish OpenART as a scalable foundation for studying agent safety in complex, evolving environments.

1 Introduction

OpenART reframes agent red teaming around executable, evolving environments to expose persistent-state and long-horizon safety failures. Its EMHA policy enables comparable evaluation across heterogeneous agents and shows that environment evolution becomes more effective as scenario complexity increases.

  • Motivation: Persistent environments couple agent decisions through repeatedly modified shared state, creating delayed safety risks that static, short-horizon benchmarks cover only weakly.These risks include persistent state manipulation, delayed attack propagation, and long-range failures emerging as environments evolve.
  • OpenART: OpenART treats the executable environment as the fundamental red-teaming unit, generating over 10K validated scenarios from more than 500K Tools, MCPs, and Skills across 50 domains.Scenarios ground benign objectives and hidden safety contracts in validated dependency graphs.
  • OpenART: OpenART preserves task semantics and evaluation criteria across 75 agent–model configurations through target-agnostic scenarios and lightweight runtime adapters.Adapters support heterogeneous agents, foundation models, interfaces, and eight target-visible attack vectors.
  • EMHA: EMHA performs feedback-driven environment evolution as a black-box policy, changing only target-visible state while keeping the task objective and hidden safety contract fixed.The policy coordinates authorized state transitions without parameter updates and progressively increases scenario difficulty.
  • Findings: 85.0% pooled Strict ASR is achieved across 75 agent–model configurations, while environment evolution improves ASR by 17.2–17.6% on the most complex scenarios versus instruction-only evolution.The improvement is only 1.8–2.7% on the simplest scenarios, and agent implementation also contributes substantially to safety variation beyond the foundation model.

2 Related Work

Prior benchmarks evaluate agents in executable, often long-horizon environments, while safety research broadens coverage across environment surfaces and adaptive red teaming. However, released tasks are typically fixed, attacks often target individual surfaces, and existing agent-level approaches only partly address iterative environment evolution.

  • Interactive environments and complex tasks: Interactive benchmarks such as AgentBench, WebArena, WorkArena, OSWorld, and TheAgentCompany evaluate executable environments rather than isolated responses.OSWorld 2.0 shows that capability conclusions can change when tasks require sustained interaction.
  • Interactive environments and complex tasks: Fixed task instances limit existing benchmarks’ ability to evaluate risks from environment-state changes during execution.These benchmarks establish the importance of environment state and task complexity, but their released instances remain fixed during evaluation.
  • Agent safety across environment surfaces: Agent-safety studies cover attacks through external observations, tools, prompts, planning, memory, Skills, MCP metadata, and long-term memory.Examples include InjecAgent, AgentDojo, ToolEmu, AgentHarm, Agent Security Bench, and later work on Skills, MCP tool metadata, and environment-induced memory.
  • Agent safety across environment surfaces: Most existing evaluations focus on a single attack surface or agent interface, limiting analysis of interacting attack vectors across heterogeneous runtimes.The related work argues that benign tasks can become unsafe through execution state observed over time.
  • Adaptive red teaming and evolving environments: Red-teaming research spans model-level adversarial-input optimization, feedback-driven iterative attacks, unified execution frameworks, and agent-level full-stack simulated services.The cited methods include GCG, AutoDAN, PAIR, TAP, AutoDAN-Turbo, X-Teaming, EvoSynth, OpenRT, and DTap-Red.

3 OpenART Arena

OpenART evaluates agent safety at the scenario level, using executable long-horizon environments with persistent state, target-specific runtime adapters, and controlled state evolution. Its validated corpus spans 50 domains and 10K scenarios while preserving each scenario’s task objective and safety contract across heterogeneous runtimes.

  • Scenario abstraction: OpenART treats the scenario—not an individual prompt—as the evaluation unit, combining a benign objective, executable environment, and hidden safety contract.Environment evolution can change execution state while preserving the underlying task and safety semantics.
  • Scenario abstraction: The domain defines a recurring setting, the seed a concrete situation, and the planner derives the task, initial environment, evaluator, and runtime representation.Environment evolution changes observed execution state without altering the scenario objective or safety criterion.
  • Corpus construction: 50 domains and a 10K corpus are built from executable workflows and admitted only after successful execution and evaluator verification.The capability registries include standardized Tools, MCPs, and Skills; validation checks whether safe and unsafe behavior are reliably distinguished.
  • Runtime projection: Target adapters project agent-independent scenarios into native runtime interfaces while preserving scenario semantics and evaluators across heterogeneous agents.Each runtime is associated with an adapter that launches the unmodified agent and maps the shared scenario into locations it natively consumes.
  • Environment evolution: Authorized environment evolution modifies target-visible state through runtime-supported attack vectors and validated native locations.Supported state surfaces include Workspace, Instructions, Skills, Tools, MCPs, Short-Term Memory, Plan State, and Long-Term Memory.

4 Red Teaming Framework

OpenART red-teams agents by evolving only the authorized, target-visible environment while keeping task objectives, safety criteria, and evaluators fixed. Its reference policy, EMHA, uses frozen models, black-box feedback, hierarchical hypergraph paths, and external state to search for effective long-horizon attacks.

  • Environment Evolution: OpenART keeps each benign objective and safety contract fixed while progressively modifying only authorized state visible to the target agent.This isolates safety failures caused by increasingly adversarial environment conditions rather than changes to the task or evaluation criteria.
  • Hypergraph Attack: At each round, EMHA retrieves context, selects a candidate hypergraph and path, decodes the path into an environment update, and incorporates evaluator feedback.The resulting hierarchical action sequence represents coordinated attack subgoals and prerequisite dependencies.
  • EMHA: EMHA is a black-box policy that coordinates environment changes whose effects may emerge across long-horizon workflows using evaluator feedback.It guides subsequent proposals without accessing or modifying the target model.
  • Frozen Adaptation: Both target and attacker models remain frozen, while EMHA performs test-time adaptation through an external attacker state containing feedback, path values, and an evolving graph pool.The external state enables in-context adaptation without parameter updates.
  • Search and Evolution: EMHA balances exploration and exploitation through feedback-guided transition values, while archive-guided graph evolution preserves high-fitness behavior cells and expands dependency structures.This produces a black-box evolutionary search over graph policies while leaving the attacker model unchanged.

5 Experiments

The experiments show that OpenART evaluates agents in validated, stateful, long-horizon scenarios and that coordinated environment evolution exposes substantial, delayed safety failures. EMHA achieves high attack success, with its advantage increasing alongside environment and workflow complexity.

  • Experimental setup: 10K validated scenarios span 50 domains and are evaluated across 75 agent–model configurations drawn from 15 deployed agents and five foundation models.The scenario pool contains over 500K tools, MCPs, and skills, while only executable bundles passing evaluator validation are included.
  • Experimental setup: 97 median tool calls, dependency depth 32, and parallel width 12.5 characterize OpenART’s long, branching workflows.OpenART’s median workflow is substantially longer than the 1–15 tool calls reported for prior benchmarks, while richer state-object and file-format counts indicate persistent state complexity.
  • Aggregate results: 85.0% pooled Strict ASR is accompanied by 73.6% of variation attributed to the target model and 25.2% to the target agent.Target-model identity and benign completion explain 91.3% of variation; adding target-agent identity raises this to 98.9%, a gain of 7.6%.
  • Attack-vector ablations: 94.7% Full EMHA Strict ASR surpasses instruction-only evolution by 13.1% and the strongest single-vector attack by 2.2%.Workspace evolution reaches 92.5% Strict ASR, while the remaining seven attack vectors average 71.2%; removing the archive or credit redistribution reduces Strict ASR by 3.9% and 2.6%.
  • Environment evolution: 42.9% first-round Strict ASR rises to 94.7% across five evolution rounds, showing that iterative environment changes expose failures missed by static evaluation.Using DeepSeek-V4-Pro, cumulative Strict ASR increases through 69.4%, 73.5%, and 89.8% in subsequent rounds.
  • Complexity and failure mechanisms: 17.2–17.6% Full EMHA advantage emerges in the most complex groups, up from 1.8–2.7% as dependency depth and tool calls increase.Environment changes often remain latent: median propagation distance is 37 target actions, with evolved-state consumption at 23% and the first unsafe output at 64% of execution.

6 Conclusion

OpenART is a large-scale arena for evaluating agent safety in complex, evolving environments. It uses validated scenarios and EMHA to evolve target-visible environments while fixing task objectives and safety contracts.

  • OpenART evaluates agent safety in complex, evolving environments through a large-scale arena.
  • 10K validated scenarios span 50 domains in OpenART’s evaluation framework.
  • EMHA performs feedback-guided environment evolution without updating model parameters.
  • OpenART keeps task objectives and safety contracts fixed while systematically evolving the target-visible environment.

A Domain Taxonomy and Scenario Collection

OpenART defines a normalized taxonomy of recurring work areas before generating scenarios, then validates scenario seeds through workflow, capability, loading, and safety-evaluation checks. The resulting corpus contains 10K validated scenarios spanning 50 domains.

  • Domain taxonomy: OpenART defines domains as recurring work areas supporting multiple tasks, workflows, and environment configurations, while merging overlapping labels into normalized domains.Candidate domains come from O*NET occupational and work-activity taxonomies and interactive agent benchmarks.
  • Scenario collection: 10K validated scenarios form the resulting corpus after workflow resolution, successful task-bundle loading, and evaluator probes that distinguish safe completion from unsafe leakage.Each scenario seed includes a scenario model, workflow graph, workspace, and hidden evaluator.
  • Scenario collection: Domain and scenario identifiers serve as bookkeeping keys and do not encode difficulty or target-agent performance.This qualification applies to the identifiers used in the corpus.
  • Domain taxonomy: 50 domains comprise OpenART’s complete domain taxonomy.The listed domains include credit and investment, accounting, taxation, legal and contracts, healthcare, education, and scientific research.

A.1 Representative Scenario Seeds … E Case Studies of Environment-Mediated Failure

OpenART turns domain-specific seeds into validated, executable scenarios with selected capabilities and runtime-specific interfaces, then studies how evolving environment state can mediate agent safety failures. The framework preserves fixed objectives and evaluators while supporting attacker-side environment manipulation and realistic control artifacts.

  • A.1 Representative Scenario Seeds: Each scenario seed specifies an actor, intended operation, capability families, delivery constraints, and requested artifact, which the planner expands into a grounded executable task bundle.The resulting seeds span diverse domains and situations across the scenario corpus.
  • B.1 Object Hierarchy: OpenART separates scenario semantics from runtime realization: planners define objectives, workflows, initial environments, and evaluators, while adapters materialize each evolving environment state.Evolution rounds change environment state without changing the task objective or evaluator.
  • B. Arena Catalog and Provenance: Accepted task bundles preserve domains, seed identifiers, actors, artifacts, workflows, capabilities, evaluator contracts, validation reports, and content hashes for traceable provenance.The manifest provides traceability from each result to its originating scenario and materialized environment.
  • B.2 Capability Corpus: The capability corpus collects Tools, MCPs, and Skills, normalizes and deduplicates their metadata, and materializes only planner-selected, validated capabilities into scenario-local stores.Recorded names and hashes preserve provenance from collected sources to executable scenarios.
  • B.3 Target Agents and Attack Vectors: Runtime adapters cover diverse coding agents and map each supported attack vector to a target-visible path or capability record before materialization.The evaluation includes OpenCode, Aider, Claude Code, Codex, Continue CLI, Copilot CLI, CodeWhale, Goose, Hermes, Kilo, Nanobot, Oh My Pi, OpenClaw, Pi, and Qwen Code.
  • C Planner Prompt Construction: The planner first binds benign objectives to approved and protected resources, publication sinks, and workflow stages, then generates and validates the complete task bundle.Validation accepts the bundle or returns structured feedback for repair, with prompts grounded in scenario seed, complexity, and available capabilities.
  • E Case Studies of Environment-Mediated Failure: OpenART’s environment-mediated failures arise when evolving workspace and service state exposes confidential material through public outputs, uploads, repositories, messages, tickets, or external services.The generated environments are designed as lived-in workflows containing public decoys, multiple protected files, service artifacts, and evaluator-detectable leak markers.
  • D EMHA Attacker Prompt Construction: Attacker rewrites supplement rather than replace standard control artifacts, because the rewrite plus CLAUDE.md, SKILL.md, rules, and memory notes together form the scenario.The doctrine explicitly states that producing the rewrite alone rarely persuades the test runner.

E.1 GPT-5.5: Safety-Debt Accumulation Attack

GPT-5.5’s safety-debt accumulation attack shifts the decision from field sensitivity to schema-required status, allowing unresolved data minimization to propagate into the final handover package.

  • E.1 GPT-5.5: Safety-Debt Accumulation Attack: The evolved finance schema marks customer emails, vendor costs, confidential pricing, national identifiers, and partial card numbers as required fields.These fields are introduced while reconciling payment, communication, calendar, and planning records into a weekly handover package.
  • E.1 GPT-5.5: Safety-Debt Accumulation Attack: Schema compliance becomes sufficient evidence that sensitive selected fields are safe, despite recognizing the sensitivity of the source records.The safety decision therefore turns on whether the schema marks a field as required rather than on the field’s inherent sensitivity.
  • E.1 GPT-5.5: Safety-Debt Accumulation Attack: The unresolved minimization decision becomes safety debt that propagates through assembly and verification into the final package.The debt persists across the workflow rather than being resolved before the handover package is completed.

E.2 Opus-4.8: Plan–Referent Drift Attack

The Plan–Referent Drift Attack shows that Opus-4.8 can follow a stable reporting workflow and template path while environment evolution changes the resolved template’s meaning and safety semantics.

  • Scenario setup: Opus-4.8 compiles a weekly public-service report from distributed records across two evaluated states.The high-level reporting workflow and canonical template path remain stable in both states.
  • Environment evolution: Environment evolution changes the template resolved at the existing path and adds a schema mapping protected records to report fields.The new field-mapping schema connects protected records with report fields.
  • Attack result: A stable path does not ensure stable safety semantics when evolution changes the object resolved by an already-planned template completion step.The successful state changes the meaning of that planned step.

E.3 Opus-4.8: Template-Authority Override Attack

The template-authority override attack exploits inherited trust in a canonical report footer within a 90-node, multi-system reporting workflow. It reveals selective trust failure: identical information is discarded as untrusted input but retained when embedded in procedurally authoritative template context, without changing the reporting objective.

  • Attack setup: A 90-node workflow integrates Freshdesk, Intercom, Salesforce, email, calendar, and document records into a regional weekly report.The evolved environment does not explicitly request disclosure of protected information.
  • Attack setup: The report template and runtime context frame an “automated footer” as trusted metadata that should remain unchanged.This inherited trust propagates into the published report.
  • Failure mechanism: Information treated as untrusted input is discarded, whereas the same information is retained when embedded in a canonical template conveying procedural authority.The target distinguishes sources by procedural role rather than content sensitivity.
  • Failure mechanism: Environment evolution exposes this selective trust failure without altering the underlying reporting objective.The attack changes environment state and contextual authority rather than the task objective.

E.4 GPT-5.5: Compliance-Rebinding Attack

For GPT-5.5, EMHA exposes a compliance-rebinding failure by evolving a safe regional-report environment into one that frames a protected source as necessary for regulatory compliance. The task objective, target model, and evaluator remain fixed while environmental framing changes the outcome.

  • E.4 GPT-5.5: Compliance-Rebinding Attack: Initially, GPT-5.5 completes the regional report without exposing any protected marker.The initial environment is safe.
  • E.4 GPT-5.5: Compliance-Rebinding Attack: EMHA evolves the environment into a compliance-framing state where audit/security_scan_results.log appears to provide the required CC-14 field.The evolved state changes how the source is presented without changing the report objective.
  • E.4 GPT-5.5: Compliance-Rebinding Attack: Strict attack success occurs because the unsafe source acquires authority by appearing necessary for regulatory compliance.This paired transition holds the task objective, target model, and evaluator fixed while changing the environment.

E.5 GPT-5.5: Provenance-Composition Attack · E.6 Controlled MCP Capability-Rebinding Attack

E.5 presents a long-horizon provenance-composition attack in which evolved provenance framing causes unsafe disclosure during report construction. E.6 defines a controlled MCP capability-rebinding attack with a matched control isolating rebinding risk from content mutation.

  • E.5 GPT-5.5: Provenance-Composition Attack: 96-node workflow reconciles records across Notion, Airtable, Snowflake, BigQuery, Databricks, Jira, Slack, and other services.The compliance task initially summarizes sensitive information without reproducing protected markers.
  • E.5 GPT-5.5: Provenance-Composition Attack: The initial execution summarizes sensitive information without reproducing protected markers, establishing the paired comparison’s safe starting point.
  • E.5 GPT-5.5: Provenance-Composition Attack: The unsafe outcome emerges only after several intermediate workflow steps rather than immediately after environment evolution.This demonstrates that the failure develops across a long-horizon workflow.
  • E.5 GPT-5.5: Provenance-Composition Attack: Evolved state changes provenance framing, altering which records the target treats as authoritative before those records are merged into the final report.The failure materializes during final report construction, not at the initial state change.
  • E.6 Controlled MCP Capability-Rebinding Attack: The controlled MCP attack asks an agent to publish an approved report through publish_report and verify the receipt while task, plan, capability name, and report remain fixed.
  • E.6 Controlled MCP Capability-Rebinding Attack: The matched control fixes capability binding while evolving all other state, separating capability-rebinding risk from ordinary content mutation.The protocol is intended to validate the fifth attack in future experiments.
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