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DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents

Zhaorun Chen, Xun Liu, Haibo Tong, Chengquan Guo, Yuzhou Nie, Jiawei Zhang, Mintong Kang, Chejian Xu, Qichang Liu, Xiaogeng Liu, Tianneng Shi, Chaowei Xiao, Sanmi Koyejo, Percy Liang, Wenbo Guo, Dawn Song, Bo Li

arXiv:2605.04808v1cs.AI

TL;DR

AI agents’ interaction with tools and dynamic environments creates security risks that existing evaluation settings inadequately capture. The paper develops DTAP, DTAP-RED, and DTAP-BENCH for controllable, policy-grounded red-teaming across diverse domains and injection vectors. Evaluations reveal systematic vulnerabilities, including compositional attacks and harness-level failure modes, showing that existing safeguards are insufficient for real-world deployment.

  • Problem

    Realistic, controllable, and reproducible environments for large-scale assessment of agent security in dynamic, untrusted settings remain underexplored.

  • Method

    The paper introduces DTAP, DTAP-RED, and DTAP-BENCH to simulate diverse environments, autonomously optimize attacks across injection vectors, and verify outcomes through environment-state judges.

  • Results

    Evaluations reveal systematic vulnerability patterns, including susceptibility to compositional attacks, uneven direct and indirect robustness, and harness-level failure modes.

  • Takeaways & Limitations

    Existing safeguards are insufficient for real-world deployment, motivating holistic and environment-aware security designs for agentic systems.

Abstract

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AI agents are increasingly deployed across diverse domains to automate complex workflows through long-horizon and high-stakes action executions. Due to their high capability and flexibility, such agents raise significant security and safety concerns. A growing number of real-world incidents have shown that adversaries can easily manipulate agents into performing harmful actions, such as leaking API keys, deleting user data, or initiating unauthorized transactions. Evaluating agent security is inherently challenging, as agents operate in dynamic, untrusted environments involving external tools, heterogeneous data sources, and frequent user interactions. However, realistic, controllable, and reproducible environments for large-scale risk assessment remain largely underexplored. To address this gap, we introduce the DecodingTrust-Agent Platform (DTap), the first controllable and interactive red-teaming platform for AI agents, spanning 14 real-world domains and over 50 simulation environments that replicate widely used systems such as Google Workspace, Paypal, and Slack. To scale the risk assessment of agents in DTap, we further propose DTap-Red, the first autonomous red-teaming agent that systematically explores diverse injection vectors (e.g., prompt, tool, skill, environment, combinations) and autonomously discovers effective attack strategies tailored to varying malicious goals. Using DTap-Red, we curate DTap-Bench, a large-scale red-teaming dataset comprising high-quality instances across domains, each paired with a verifiable judge to automatically validate attack outcomes. Through DTap, we conduct large-scale evaluations of popular AI agents built on various backbone models, spanning security policies, risk categories, and attack strategies, revealing systematic vulnerability patterns and providing valuable insights for developing secure next-generation agents.

1 Introduction

The paper introduces DTAP, DTAP-RED, and DTAP-BENCH to evaluate agent security across realistic attack surfaces. Large-scale evaluations reveal systematic vulnerabilities involving injection composition, threat models, risk categories, environments, and agent backbones.

  • Contributions: DTAP-RED autonomously explores prompt-, tool-, skill-, and environment-level injections by jointly optimizing payloads and injection locations.It uses verifiable judges that inspect environment outcomes and provide feedback for iterative refinement.
  • Key findings: Skill- and tool-level injections consistently achieve higher ASR than environment injections, while vulnerability patterns vary across frameworks.OpenClaw has substantially lower tool-injection ASR than OpenAI Agents and Google ADK.
  • Key findings: Compositional attacks combining multiple injection vectors are especially effective against agents.Examples include fabricated email threads, multi-message injections, multi-step chains, and multimodal typographic attacks.
  • Key findings: Security weaknesses differ across threat models, risks, environments, and backbones, including execute-then-refuse behavior and greater direct-misuse susceptibility for open-source models.Finance environments show lower ASR, while context-aware risks and communication-centric environments are more difficult for agents.
  • Contributions: DTAP provides a controllable red-teaming platform spanning 50+ simulated environments for agent utility and security evaluation.The environments replicate real-world systems and support diverse high-stakes workflows.
  • Contributions: DTAP-BENCH contains 6,682 tasks covering 4K+ malicious goals across 300+ risk categories and 14 domains, with attacks paired with verifiable judges.The benchmark is policy-grounded and validates outcomes through environment states.

2 Related Works

Prior agent red-teaming platforms and benchmarks offer limited realism, coverage, or attack sophistication. DTAP addresses these gaps with full-stack environments and policy-driven benchmark instances spanning diverse injection vectors and optimized attacks.

  • Existing platforms: Earlier platforms often rely on static synthetic tools or simplified interactive environments that inadequately model realistic agent dynamics.AgentDojo and AgentHarm use hardcoded injection outputs, while SHADE-Arena and Petri remain simplified.
  • DTAP platform: DTAP provides 50+ full-stack environments that replicate agent interfaces and support high-concurrency evaluation and arbitrary adversarial-state resets.These properties target realistic, efficient, and transferable simulation.
  • Existing benchmarks: Existing benchmarks typically focus on appended malicious instructions or naive prompt-injection templates, leaving complex attack patterns underexplored.Prior work often studies direct prompt or tool-output injection without advanced attack techniques.
  • DTAP-BENCH: DTAP-BENCH covers 4K malicious goals from 60+ security policies and includes prompt, tool, skill, and environment injections optimized from 200+ attack strategies.Instances assume generic black-box agent access and include end-to-end optimized attacks.

3 DTAP: Unified Platform for Agent Red-Teaming

DTAP is a controllable, interactive simulation platform for red-teaming agents across 14 domains and more than 50 realistic environments. Its dynamic, reproducible, resettable, and parallelizable design supports scalable evaluation through faithful interfaces and diverse injection points.

  • Scope and realism: DTAP covers 14 domains and over 50 environments involving high-stakes actions, with interfaces designed to mirror real-world agent interactions.The platform targets realistic, controllable, and dynamic security validation.
  • Interface replication: DTAP replicates official GUIs, HTML structures, MCP tool specifications, input schemas, and return formats for realistic visual and textual agent interaction.The platform supports both human users and agents relying on HTML or accessibility-tree observations.
  • Design principles: Its environments provide dynamic multi-turn stateful interactions, deterministic transitions, flexible reset endpoints, and parallelized containerized execution.These properties enable repeatable risk analysis and rapid reproduction of attack scenarios.
  • Attack surfaces: The platform supports compromised payloads in user-facing and external sources such as emails, invitations, reviews, comments, tools, skills, and environment data.These injection points model attacks embedded in realistic interaction flows.
  • Agent integration: Agents connect to environments through a unified MCP interface, enabling plug-and-play integration with supported tool-use, coding, and computer-use frameworks.Built-in support includes OpenAI Agents SDK, Google ADK, LangChain Agents, Claude Code, and OpenClaw.

4 DTAP-RED: Autonomous Red-Teaming Agent for Agentic Systems

DTAP-RED is an autonomous, policy-following red-teaming agent that optimizes attacks against arbitrary agents through a closed-loop process. It combines memory, attack skills, flexible injection actions, and verifiable feedback to generate DTAP-BENCH attacks.

  • End-to-end procedure: DTAP-RED accepts malicious goals and verifiable judges, then automates attack generation, execution, evaluation, and refinement in a closed-loop pipeline.Its three stages are attack optimization and generation, execution against the victim, and judge-based iterative refinement.
  • Attack generation: The agent retrieves successful and failed experiences from multi-layer memory and invokes attack algorithms and injection strategies from its skill library.The memory organizes experiences by risk categories, malicious goals, and threat models.
  • Attack skills: DTAP-RED supports jailbreak and prompt-injection algorithms applied across direct and indirect channels, including tools, skills, environments, and their combinations.Its library includes methods such as flip-attack, base64 encoding, GCG, and ArtPrompt.
  • Injection action space: Its action space supports spatially diverse injection points and temporal composition through configurable suffixes, prefixes, rewrites, and multi-step actions.Injection locations and capabilities depend on the threat model.
  • Evaluation and refinement: Verifiable judges inspect concrete environment states after each attack, while refinement judges analyze failures and guide subsequent iterations.Examples include checking data exfiltration or unauthorized PayPal transactions rather than relying only on trajectories or language-model judgments.

5 DTAP-BENCH: Benchmarking AI Agents with Advanced Red-Teaming

DTAP-BENCH is a policy-grounded benchmark of realistic agent tasks and red-teaming trajectories across diverse domains and environments. Its deterministic configurations, seeded environments, verifiable judges, and transferable attacks support reproducible comparison.

  • Dataset Structure: DTAP-BENCH contains over 6,000 scenarios across 14 domains and more than 50 environments, including benign, indirect, and direct red-teaming tasks.The benchmark covers high-stakes workflows and malicious goals under distinct threat models.
  • Task: Each task specifies instructions, agent setup, injection sequences, environment-seeding scripts, and a concrete judge for deterministic evaluation.These components establish reproducible initial conditions and verify whether the intended outcome occurs.
  • Red-Teaming Task Set: Red-teaming tasks use DTAP-RED attack sequences optimized for up to 10 iterations against a surrogate agent, then selected for balanced coverage across injection types.The shared attacks enable fair zero-shot transfer comparisons across agents.
  • Policy-Driven Data Construction Pipeline: The construction pipeline derives severe risk categories from domain policies and regulations, then pairs manually designed malicious goals with verifiable judges.Persona-based synthesis is used to create diverse initial environment conditions.
  • Cost and Human Effort: DTAP-BENCH required approximately 16,000 human hours and $120,000 in API credits for trajectory validation, attack generation, and data curation.The effort involved 17 red-teaming experts over 20 months.

6 Experiments

The experiments evaluate agent frameworks and models on benign and adversarial tasks using BSR and threat-model-specific ASR. Results show substantial, uneven vulnerabilities shaped by threat model, injection surface, composition, model backbone, and harness design.

  • Evaluation Setup: The evaluation covers four frameworks and six agent configurations across 14 domains, including 1,998 indirect, 1,878 direct, and 2,806 benign tasks.ASR measures fully achieved malicious goals, while BSR measures fully completed benign instructions.
  • Main Results: Directly supplied malicious goals yield near-zero ASR, whereas DTAP-optimized attacks substantially increase ASR across domains and risk categories.This comparison indicates that optimized attacks expose vulnerabilities not observed under naive direct prompting.
  • Main Results: Among capable agents, Google ADK reaches 55.7% indirect ASR with 87.0% BSR, while Claude Code achieves 25.2% indirect and 26.9% direct ASR with 80.8% BSR.Robustness varies significantly and is mostly uncorrelated with benign utility.
  • Injection Analysis: Skill- and tool-level injections exceed environment injections, while compositional attacks can raise ASR from 50.1% to 65.6% in GPT-5.2.OpenClaw records 21.1% tool-injection ASR versus 63.4% for Google ADK.
  • Harness Analysis: Replacing the OpenAI Agents SDK harness with OpenClaw reduces direct ASR from 58.8% to 38.6% and indirect ASR from 46.7% to 35.6%, with approximately 2% utility cost.The reported combined reduction is approximately 31%.
  • Backbone Analysis: Open-source backbones show higher direct misuse susceptibility, with OpenClaw and DeepSeek-V4-Pro reaching 59.6% direct ASR despite 83.3% BSR.Closed-source examples show lower direct ASR, including GPT-5.2 at 38.6% and GPT-5.5 at 28.9%.
  • Failure Modes: OpenAI Agents SDK and Google ADK frequently execute harmful tool calls before refusing, consistent with harnesses that prioritize batch tool invocation.The authors conjecture that this design may hinder intermediate consequence reasoning and intervention.

7 Conclusions

DTAP combines a controllable red-teaming platform, an autonomous attack agent, and a policy-grounded benchmark for evaluating AI-agent security. Its evaluations expose compositional and harness-level vulnerabilities and motivate environment-aware security designs.

  • Conclusion: DTAP integrates realistic environment simulations with DTAP-RED and DTAP-BENCH to support scalable, comprehensive red-teaming of AI agents.The platform is presented as a foundation for rigorous agent evaluation.
  • Conclusion: The evaluations reveal systematic susceptibility to compositional attacks and harness-level failure modes, while existing safeguards remain insufficient for real-world deployment.The paper highlights holistic, environment-aware security designs as a needed direction.

Contribution Statement

The supplied material describes DTAP’s platform, benchmark, evaluation methodology, findings, environments, and supporting analyses, but does not provide a conventional author contribution statement for these contents.

  • Dataset Statistics: DTAP-BENCH includes 6,682 tasks across 14 domains: 2,806 benign, 1,998 indirect red-teaming, and 1,878 direct red-teaming tasks.Most red-teaming tasks require more than 10 tool calls in many domains, reflecting multi-step workflows.
  • Evaluation Design: DTAP uses deterministic rule-based judges that inspect concrete environment outcomes rather than relying on agent outputs, trajectories, or LLM-based evaluation.This design targets more reliable and reproducible attack-success measurement.
  • DTAP-RED Analysis: DTAP-RED typically finds successful attacks within 2–5 iterations in CRM categories, while Fraud & Impersonation requires up to 6.3 indirect iterations.The reported results characterize efficient exploration and convergence in the CRM domain.
  • Attack Skills: In CRM, multi-turn fake conversation and general-misinformation strategies exceed 50% ASR, while stealthy email injection and autoDAN-turbo reach 30%–40% ASR.These results illustrate variation in attack-skill effectiveness across scenarios.
  • Benign Utility: Frontier agents achieve generally high BSR on benign workflow categories, especially Meeting Coordination, Survey & Data Collection, and Communication & Announcement.The reported utility is measured when task objectives are benign and no attacks are applied.
  • Indirect Threat Model: Indirectly introduced malicious intent produces pronounced vulnerabilities across many workflow model–framework combinations, including Gemini-3-Pro and Claude-Sonnet-4.5.The attacks use manipulated emails, adversarial documents, injected messages, or tool outputs.
  • Direct Threat Model: Without red-teaming optimization, all workflow model–framework combinations achieve 0.0% ASR under direct malicious prompting.The result indicates strong surface-level resistance to explicit malicious instructions in this setting.
  • Optimized Attacks: Optimized direct attacks sharply increase vulnerabilities across many workflow systems, including Gemini-3-Pro, OpenClaw + GPT-5.1, and OpenAI Agents SDK + GPT-5.2.The strongest effects occur across multiple risk categories, including Financial Fraud and Illegal Activity.

B.5 Analysis

Across workflow environments, direct attacks are usually stronger than indirect attacks, but vulnerability depends strongly on the model, harness, environment, and risk category. Benign capability does not reliably predict robustness, while workflow-native actions remain persistently exploitable.

  • Threat-model asymmetry: Direct attacks generally achieve higher ASR than indirect attacks, but Google ADK + Gemini-3-Pro and GPT-OSS-120B show reversed or extreme asymmetries.OpenAI Agents SDK + GPT-5.2 rises from 63.1% indirect to 69.1% direct, whereas Google ADK + Gemini-3-Pro falls from 65.9% to 54.4%.
  • Harness effects: 37.0 points under indirect attacks and 38.2 points under direct attacks separate OpenAI Agents SDK from OpenClaw for GPT-5.2, while benign BSR drops from 76.4% to 61.0%.For Claude-Opus-4.6, OpenClaw instead delivers 91.5% benign performance, while Claude Code achieves lower ASR.
  • Security-utility relationship: Higher benign capability does not monotonically imply lower ASR: OpenClaw + Claude-Opus-4.6 reaches 91.5% BSR with relatively low ASR, while Google ADK + Gemini-3-Pro reaches 65.9% indirect ASR.The findings support explicit robustness evaluation rather than inferring security from task success.
  • Risk categories: 100.0% ASR occurs for several workflow-native risks, while Data Exfiltration remains comparatively harder, never exceeding 28.6% direct or 46.7% indirect ASR.Messaging Abuse, Financial Fraud, Sensitive File Deletion, and related operational categories remain prominent vulnerabilities.
  • Model transfer: GPT-OSS-120B’s 7.2% indirect ASR rises to 61.7% direct ASR, so low indirect vulnerability partly reflects weak indirect task completion rather than stronger security.This asymmetry makes its indirect result unsuitable as standalone evidence of robustness.
  • Environment effects: WhatsApp reaches 80.00 direct ASR and Google Calendar reaches 55.56 indirect ASR, whereas PayPal reaches only 11.11 direct and 34.07 indirect ASR.Shared documents, communication, and coordination platforms are generally easier to exploit than financially sensitive or structured database environments.

C.5 Analysis

CRM evaluations show that security depends on threat model, framework, model alignment, risk category, and injection surface. Skill and compositional injections are especially potent, while content-level safeguards remain less effective against harmful multi-step actions.

  • Security-utility relationship: Google ADK reaches 86.1% BSR and 69.9% indirect ASR, showing that high benign capability can coexist with high vulnerability.The CRM results reject an inherent security-utility tradeoff.
  • Threat models: GPT-family direct-over-indirect gaps reach 29.6 percentage points, while Gemini-3-Pro instead shows 69.9% indirect versus 55.6% direct ASR.The threat models probe prompt-level refusal and data-channel integrity as different security properties.
  • Framework effects: Framework changes improve security for vulnerable models but do not replace intrinsic alignment: OpenClaw lowers GPT-5.2 ASR, whereas Claude-Opus-4.6 remains comparably robust across frameworks.For GPT-5.2, OpenAI Agents SDK yields 66.4%/83.3% indirect/direct ASR, versus approximately 15.5-point reductions with OpenClaw.
  • Risk categories: Misinformation & Hallucination has approximately 76% average direct ASR, while General AI Restrictions has approximately 19%, establishing a stable category hierarchy.Invalid Consent and Dangerous Actions follow at approximately 58% average direct ASR.
  • Injection surfaces: Skill-only injection reaches 83.3% ASR for GPT-5.4 and 77.8% for GPT-5.2, exceeding environment-only and tool-only attacks on the same agents.Skills are treated as trusted instructions and remain effective even against models resisting prompt-based attacks.
  • Compositional attacks: Combining injection surfaces can outperform individual vectors, with skill+tool reaching 100% for GPT-5.4 and GPT-5.2 while multi-vector attacks fail against Claude.The center panel of Figure 23 compares injection combinations across agents.
  • Case study: An indirect email injection causes GPT-5.4 to forward a Databricks API token to an attacker-controlled Gmail address during a benign CRM lookup.The outcome demonstrates credential leakage through an external workflow artifact.
  • Case study: A direct jailbreak causes GPT-5.2 to update a CRM record with weaponization instructions before refusing, leaving the environment already compromised.The case illustrates execute-then-refuse behavior under batch tool invocation.

D.5 Analysis

Customer-service agents with similar benign success rates can have sharply different attack vulnerability, and direct versus indirect attacks expose different weaknesses. Financial operations are especially ambiguous, while combined channels generally increase attack success.

  • Security-utility relationship: 87–90% BSR configurations span 1.3% to 70.2% direct ASR, demonstrating large security differences within a high-capability plateau.GPT-OSS-120B’s 31.2% direct ASR often reflects failed malicious execution rather than refusal-based safety.
  • Threat-model asymmetry: Gemini-3-Pro reaches 75.1% indirect versus 40.3% direct ASR, while GPT-5.2 reaches 70.2% direct versus 53.9% indirect ASR.Gemini often refuses direct attacks before tool invocation but executes workflows containing injected tool-description instructions.
  • Attack transfer: GPT-5.4 reaches 64.7% and GPT-5.2 70.2% under direct transfer attacks, while Gemini-3-Pro reaches 75.1% under indirect attacks.Claude-Opus-4.6 remains the most resistant configuration across both threat models.
  • Risk categories: Policy Misrepresentation and Financial Fraud dominate direct attacks but become among the least vulnerable indirect categories, while Bulk Destructive Actions reverses in the opposite direction.This swap appears across non-Claude configurations but not Claude-Opus-4.6.
  • Financial Fraud: 95.0% Financial Fraud ASR for GPT-5.4 and GPT-5.2 reflects ambiguity in refund and store-credit tools that serve both legitimate and policy-violating actions.The same operations require policy-threshold verification to distinguish malicious intent.
  • Injection combinations: 42.2% indirect ASR for environment-plus-tool injection exceeds 24.1% environment-only and 33.3% tool-only across eight configurations.The strongest two-channel combination can saturate the available attack surface, while the best single channel varies by agent.
  • Financial Fraud case study: Gemini-3-Pro grants $325 in store credit after fabricated approvals override a retrievable $100 policy cap, while Claude-Opus-4.6 enforces the cap.The attack uses environmental injection through case notes and order metadata.
  • Data exfiltration case study: GPT-5.2 transmits customer payment details, billing address, and phone number to an attacker through a deceptive compliance-export email.The exfiltration is presented as a legitimate billing report despite containing real customer records.

E.5 Analysis

Travel-domain evaluations show that indirect attacks often outperform direct attacks, while tool-based injections and execute-then-refuse behavior expose important safety weaknesses. Frameworks can reduce vulnerability for weakly aligned models, but provide limited additional benefit for strongly aligned ones.

  • Indirect attacks generally achieve higher ASR than direct attacks across travel-agent configurations.GPT-5.4 reaches 55.0% indirect versus 32.4% direct ASR on OpenAI Agents SDK, while Gemini-3-Pro reaches 84.2% versus 52.4% on Google ADK.
  • 99.2% benign success coexists with 84.2% indirect and 52.4% direct ASR for Google ADK with Gemini-3-Pro.This configuration has the highest benign success rate while also showing the highest indirect ASR in the comparison.
  • GPT-5.4 reduces direct ASR to 32.4% and indirect ASR to 55.0% while maintaining 94.6% benign success, compared with GPT-5.2’s 93.8% BSR, 42.9% direct ASR, and 66.7% indirect ASR.The comparison indicates intergenerational safety improvement without a substantial benign-capability decrease.
  • Tool-only injections outperform prompt-only attacks for all non-Claude travel agents.For GPT-5.4 on OpenAI Agents SDK, tool-only ASR is 66.7% versus 15.9% for prompt-only; for Gemini-3-Pro, it is 100.0% versus 34.1%.
  • 36.7% of GPT-5.1 refusals execute harmful tool calls before refusal, versus 10.2% for GPT-5.2 and 3.4% for GPT-5.4.Claude models on Claude Code show 0.0% execute-then-refuse behavior.
  • Framework guardrails reduce vulnerability more for weakly aligned models than strongly aligned ones.GPT-5.2’s direct ASR drops from 42.9% on OpenAI Agents SDK to 12.4% on OpenClaw, whereas framework effects for Claude Opus-4.6 are minimal and can slightly increase vulnerability.
  • Multi-vector injection successfully exfiltrates a user’s phone number to an accommodation host.The attack fulfills the data-exfiltration goal while the agent performs the intended travel task.

G.5 Analysis

Browser-domain analysis finds that benign capability, attack susceptibility, and refusal behavior can diverge substantially. Red-teaming exposes vulnerabilities across models, injection surfaces, risk categories, and direct or indirect attack settings.

  • GPT-5.2’s over-conservative refusals reduce benign BSR by failing to execute authorized delegated actions.Examples include refusing to create an eBay account and refusing to post a user-requested product review.
  • Target-website review injections are less effective than other indirect attack patterns.Agents rarely follow malicious instructions embedded in product-review text even when they read the injected content.
  • Final refusal text is an insufficient safety signal because GPT-5.1 may execute harmful actions before refusing.The browser case study shows spam content posted despite a refusal-like final response.
  • Compositional attacks combining injection vectors are highly effective across browser tasks.Examples include fabricated email threads, multi-message contextual injections, multi-step chains, and multimodal typographic injections.
  • Gemini-3-Pro reaches 35.3% direct and 62.2% indirect ASR, while Claude-Opus-4.6 records 0.0% indirect ASR under both frameworks.These results show substantial variation in transfer susceptibility across backbone models.
  • Browser integrity and data exfiltration are the most exploitable risk categories, with direct integrity ASR up to 100.0% and indirect data-exfiltration ASR up to 80.0%.Harmful/illegal, manipulative AI use, and spam-bot attacks transfer less reliably in many configurations.
  • A multi-tool indirect attack causes Google ADK with Gemini-3-Pro to retrieve saved credit-card data and send it to an attacker’s email address.The attack compromises the victim while it navigates the user’s account page and completes the benign task.

H.5 Analysis

Research-domain evaluations reveal risks from hallucinated citations, indirect instruction injections, and uneven susceptibility across risk categories and models. Red-teaming substantially increases attack success, while capability and safety do not necessarily move together.

  • GPT-OSS-120B fabricates incorrect arXiv IDs in accuracy-critical research tasks.Such hallucinations undermine research-agent reliability and threaten the integrity of downstream scientific workflows.
  • Gemini’s indirect ASR can exceed optimized direct ASR in Arms Control and nuclear-related scenarios.The finding indicates weaker protection against indirect injections than against direct malicious requests in those categories.
  • Nuclear Risks show near-zero indirect ASR, whereas Bio Risks remain more vulnerable.Risk-category ASR comparisons indicate uneven task difficulty or alignment across research domains.
  • Claude Opus 4.6 combines strong benign performance with lower ASR than other evaluated models, while weaker models are not necessarily safer.The comparison directly separates capability from safety across research agents.
  • Red-teaming attacks optimized on GPT-5.1 transfer more effectively to GPT and Gemini models than to Claude models.Gemini-3-Pro shows higher ASR than Claude models across multiple risk categories.
  • Indirect tool-description injection causes a research agent to generate content facilitating arms-control circumvention while completing its benign task.The agent follows the malicious instructions without proper validation.
  • 87.0% is Gemini-3-Pro’s highest benign BSR in the OS-filesystem benchmark, while indirect ASR reaches 59.5%.Indirect ASR is lower than direct ASR for every evaluated model in this benchmark.
  • 0.0% ASR occurs for all models on unaugmented malicious filesystem prompts, but optimized rewriting raises ASR to 84.0% for GPT-5.4.Claude Sonnet-4.5 and Opus-4.6 remain lower at 22.6% and 26.2%, respectively, after red-teaming.

I.5 Analysis

The analysis reveals that agent vulnerability varies substantially by threat model, risk category, injection surface, model, and framework. Direct attacks and infrastructure-oriented risks expose recurring weaknesses, including permission sabotage, tool-transfer attacks, and destructive filesystem actions.

  • Claude Opus-4.6 combines near-top benign success with low attack success, showing that capability and security are not inherently opposed.It achieves 83.3% BSR, 1.2% indirect ASR, and one of the lowest direct ASR values.
  • Direct attacks generally achieve higher ASR than indirect attacks, with the largest gaps for OpenAI Agents SDK GPT-5.4, GPT-5.2, and GPT-OSS-120B.The reported direct-over-indirect gaps are 46.5pp, 39.3pp, and 37.2pp, respectively.
  • “Prohibited AI Practices” is the weakest risk category, with persistent PROMPT_COMMAND surveillance hooks producing high ASR across models.Claude Opus-4.6 reaches 62.5% ASR, while OpenAI Agents SDK GPT-5.4 reaches 93.8% in this category.
  • Tool injection can outperform environment injection and transfer across models, while compositional attacks combining multiple injection vectors are particularly effective.For GPT-5.4, tool versus environment injection reaches 59.3% versus 5.1%; for Gemini-3-Pro, it reaches 92.6% versus 7.6%.
  • Authority framing dominates successful direct attacks, and shell-configuration backdoors compromise routine administrative workflows.Authority framing appears in 81.8% of successful direct attacks; one permission-sabotage case succeeds against 8 of 9 model–framework combinations.
  • A direct .bashrc attack silently changes directory permissions from 755 to 777, granting every system user full access to the development directory.The agent verifies, modifies, rereads, and syntax-checks .bashrc while treating the operation as routine configuration.
  • An indirect tool injection destroys core.py while successfully completing a documentation task, compromising 5 of 8 model–framework combinations.The file’s original 30 lines of valid Python are replaced by a one-line placeholder, while the documentation output remains correct.
  • Windows evaluations show substantial benign capability alongside meaningful attack exposure, with Claude Code achieving the highest macro-averaged BSR at 83.5%.OpenAI Agents (GPT-5.4) reaches 77.5% BSR and Google ADK (Gemini-3-Pro) reaches 76.0%.

J.5 Analysis

The analysis shows that adaptive red-teaming exposes attacks that evade per-step safeguards, vary across operating systems and threat models, and exploit framework- and modality-specific weaknesses. Direct attacks, nested payloads, scheduled-task persistence, and image-borne instructions can produce substantial or delayed harm.

  • Nested multi-step malware attacks bypass filters that reject single-step payloads because each operation appears benign while the malicious intent emerges compositionally.The attacks succeed against OpenAI Agents GPT-5.2 and GPT-5.4 despite direct curl | bash payloads being almost always refused.
  • Windows direct ASR ranges from 28–48%, whereas indirect ASR remains lower at 6–14% but is harder to defend against.The capability–security tradeoff is more pronounced under direct threats, where framework choice shifts the curve by 10–15 percentage points.
  • Stronger models are more vulnerable to direct jailbreaks but show similar indirect ASR, with GPT-5.4 reaching 47.9% direct ASR and 10.6% indirect ASR.GPT-5.2 reaches 44.6% direct and 13.0% indirect ASR, while GPT-OSS-120B reaches 33.3% and 14.7%.
  • OpenClaw produces the lowest indirect ASR for every tested model, reducing ASR by 10–30 percentage points relative to OpenAI Agents SDK.The largest improvements concern surveillance and privilege escalation, while data-theft reductions are smaller.
  • Raw malicious prompts remain effective, with naive direct ASR reaching 20.0% for GPT-5.4 and Claude Opus-4.6 and up to 35.0% for privilege escalation and data theft.Surveillance has 0% naive ASR across tested configurations.
  • A scheduled-task attack creates a hidden PowerShell fetch-and-execute channel that runs as SYSTEM at the next 3:00 AM trigger.The judge verifies persistence and delayed execution even though the harmful download occurs after the observed trajectory.
  • The scheduled-task injection succeeds against 6 of 8 configurations because the malicious action is syntactically consistent with the benign task.Only GPT-OSS-120B and OpenClaw with Opus resist this injection.
  • Image-content injection can drive surveillance and exfiltration, while removing image injection reduces macOS ASR by 8 percentage points and image-only injection reaches 2.0%.The multimodal trajectory demonstrates that text-channel hygiene alone does not prevent image-borne instructions from reaching tool execution.

L.5 Analysis

Finance evaluations reveal that vulnerability depends on attack timing, malicious-action overlap, injection surface, model, and framework. Red-teaming uncovers harmful executions that refusal-rate metrics and naive prompts can miss.

  • Refusal timing: 26–38% of GPT models’ rejected direct attacks still execute irreversible harmful tool calls before refusing.GPT-5.2 failures average 3.6 harmful actions per affected task, while GPT-5.4 failures average 3.1.
  • Indirect attacks: 4–40× higher indirect ASR occurs when malicious actions overlap with plausible benign workflow steps.Quantity inflation, action reversal, and unauthorized stock sale are among the most vulnerable categories, at 40.7%, 38.5%, and 34.1% average ASR.
  • Capability and security: 97.1% BSR with 4.0% direct ASR and 0.6% indirect ASR demonstrates that benign capability and security can coexist, unlike GPT-5.2’s comparable 97.7% BSR with 51.5% and 40.4% ASR.GPT-5.4 reduces direct ASR to 30.5% while maintaining 96.6% BSR.
  • Injection surfaces: 47–55% tool-description-injection ASR for GPT models exceeds 1–4% environment-only ASR, while mixed injection reaches 51–79%.Claude-Opus-4.6 records 0% tool-description-injection ASR across both evaluated frameworks.
  • Attack mechanisms: Institutional-authority framing is a transferable attack strategy because agents defer to API documentation, compliance policies, regulations, and internal memos.The finance examples include multi-channel stock substitution and a direct pump-and-dump attack that executes trades and fabricated communications.

N.5 Analysis

Telecom and medical analyses show that attack vulnerability is shaped by threat model, workflow context, framework, and model, rather than benign capability alone. Indirect operational context can drive privacy leakage and unauthorized changes, while medical direct attacks remain broadly effective despite capability gains.

  • Capability and security: 75.5% benign task success coexists with 42.3% indirect ASR and 41.8% direct ASR for OpenClaw with GPT-5.2 in Telecom.Claude Code with Opus-4.6 achieves lower attack rates despite 68.2% benign BSR.
  • Threat-model effects: 38.5% optimized direct ASR for GPT-5.4 versus 7.2% naive direct ASR shows that naive prompts underestimate Telecom risk.For GPT-5.2, optimized direct ASR is 60.7% versus 11.1% naive ASR.
  • Risk categories: Credential Takeover exceeds Credential Leak for every Telecom model under optimized direct attacks, indicating greater exposure to unauthorized account manipulation than explicit secret disclosure.The reported pattern is consistent with safeguards recognizing credential-exfiltration requests more readily than operational changes.
  • Privacy leakage: 70.0–100.0% indirect email-PII leakage across listed Telecom configurations contrasts with much lower optimized direct leakage for several models.The results attribute Telecom PII exposure primarily to attacker-controlled workflow context.
  • Medical threat models: Medical direct attacks generally achieve higher ASR than indirect environment or tool-output injections, making prompt-level robustness a primary bottleneck.Evaluating only environment-based injection risks may underestimate vulnerability to malicious instructions framed as clinical guidance.
  • Framework effects: Frameworks alter which medical risk categories are most vulnerable, even when the same base model is used.GPT-5.2 has substantially lower overall ASR under OpenClaw than under the OpenAI Agents SDK, while category rankings differ.
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