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MobileJailBench: Red Queen Jailbreak Benchmarking for Mobile Agents

Songping Wang, Borui Li, Qinglong Liu, Yueming Lyu, Yi Huang, Fengting Li, Chao Feng, Caifeng Shan

arXiv:2610.23303v1cs.CR

TL;DR

Jailbreak evaluations for mobile agents largely test conversational attacks, fixed task sets, and attack success rather than realistic workflows and broader security behavior. MobileJailBench combines a mobile-task benchmark, evolving attacks and tasks, and stratified adjudication; experiments find particular vulnerability to implicit jailbreaks and report 100% ASR on Qwen3.5-122B under dynamic evolution.

  • Problem

    Existing jailbreak evaluations focus on conversational attacks, static task sets, and ASR, leaving realistic mobile workflows and broader security diagnosis insufficiently assessed.

    Source: §3 · p. 4

  • Method

    MobileJailBench combines 520 mobile tasks across jailbreak paradigms and risk categories with Dual-Dynamic Evolution and adjudication of awareness, execution behavior, and consequences.

    Source: §1 · pp. 2–3

  • Results

    Mobile agents are particularly vulnerable to implicit jailbreaks, and dynamic evolution reaches 100% ASR on Qwen3.5-122B.

    Source: §1 · p. 3

  • Takeaways & Limitations

    MobileJailBench provides a reproducible, continuously evolving framework for tracking mobile agents' security boundaries.

    Source: §7 · p. 9

    The adversary is restricted to instructions and designated environmental carriers and cannot directly mutate simulator state or tamper with system instructions, model parameters, or the evaluation harness.

    Source: §3.1 · p. 4

Abstract

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MLLM-based mobile agents are rapidly gaining autonomy in perception, planning, and closed-loop execution in dynamic mobile environments. However, this autonomy also broadens the attack surface and amplifies the risks of malicious misuse, including privacy leakage, device compromise, and large-scale phishing. Existing jailbreak evaluations leave three critical gaps for mobile agents: they focus on conversational attacks instead of mobile workflows, use static task sets with fixed difficulty, and rely narrowly on attack success rate rather than comprehensive security assessment. To close these gaps, we present MobileJailBench, the first evolving jailbreak benchmark for mobile agents, comprising three key components: (1) a benchmark comprising 520 tasks spanning three jailbreak paradigms and 14 risk categories, built on a high-fidelity mobile sandbox that we construct, enabling realistic workflows across over 20 widely used apps; (2) Dual-Dynamic Evolution, which combines Failure-Distilled Jailbreak Evolution with Weakness-Driven Benchmark Expansion; and (3) Stratified Adjudication Framework for assessing security awareness, execution behavior, and risk consequences. Extensive experiments show that current mobile agents are particularly vulnerable to implicit jailbreaks, as their safety alignment remains largely superficial and fails to generalize from conversational refusal to dynamic mobile interactions. Moreover, our dynamic evolution mechanism sharpens practical threat boundaries by continually uncovering new vulnerabilities, reaching a 100% ASR on Qwen3.5-122B. MobileJailBench provides a reproducible, continuously evolving framework for tracking the true security boundaries of mobile agents.

1 INTRODUCTION

Mobile agents can act across apps and change device state, making jailbreaks a route to concrete harms. MobileJailBench addresses gaps in workflow realism, evolving difficulty, and diagnosis beyond attack success rate.

1 INTRODUCTION

  • Existing evaluations focus on conversational attacks, static task sets, and ASR, rather than mobile workflows, adaptive attacks, and diagnosis of awareness, behavior, and consequences.These omissions matter in dynamic, stateful, cross-application environments where security failures can produce harmful actions.Source: §1 · p. 2
  • MobileJailBench combines 520 tasks across three jailbreak paradigms and 14 risk categories with a high-fidelity sandbox spanning 20+ widely used apps.Its components also include Dual-Dynamic Evolution and a Stratified Adjudication Framework.Source: §1 · pp. 2–3
  • The Stratified Adjudication Framework assesses security awareness, execution behavior, and risk consequences to distinguish failures that ASR alone cannot resolve.MobileJailBench thus pairs attack benchmarking with a multidimensional assessment of agent safety.Source: §1 · p. 3
  • Current MLLM-based mobile agents are highly vulnerable in realistic settings, with implicit jailbreaks a particularly practical attack surface, and dynamic evolution drives Qwen3.5-122B to 100% ASR.Source: §1 · p. 3

2 RELATED WORK

Research has advanced from language-based agents to autonomous mobile operators, alongside emerging work on mobile safety and security. Existing benchmarks still do not jointly capture realistic mobile grounding, diverse jailbreak strategies, and fine-grained diagnosis.

2 RELATED WORK

  • Mobile agents have progressed from sequential language-model decisions and tool use to multimodal interaction with smartphone interfaces and dynamic environments.Prior work includes AppAgent, Mobile-Agent, Mobile-Agent-v2, Mobile-Bench, OSWorld, AndroidWorld, and DigiRL.Source: §2 · p. 3
  • MobileSafetyBench, GhostEI-Bench, and OS-Sentinel study mobile safety, environmental injection, and trajectory validation, while other benchmarks address tool and agent safety.The cited work includes MobileSafetyBench Lee et al. (2026), GhostEI-Bench Chen et al. (2025), and OS-Sentinel Sun et al. (2025).Source: §2 · p. 3
  • Existing benchmarks lack a systematic mobile-agent jailbreak framework combining realistic workflows, evolving task difficulty, and diagnosis beyond binary success or failure.MobileJailBench targets this gap with a dynamic benchmark and hierarchical diagnosis of awareness, execution behavior, and risk consequences.Source: §2 · p. 3

3 MOBILEJAILBENCH: DESIGN AND CONSTRUCTION

MobileJailBench is designed to evaluate jailbreak risks in executable mobile workflows rather than prompt-only interactions. Its benchmark combines a stateful simulator, varied task paradigms, and constrained adversarial access.

3 MOBILEJAILBENCH: DESIGN AND CONSTRUCTION

  • The benchmark contains 520 tasks across 14 risk categories and six sub-benchmarks, covering explicit, implicit, and composite jailbreaks at workflow levels L1–L4.Of the tasks, 322 involve cross-application interactions.Source: §3 · p. 4

3.1 THREAT MODEL AND MOBILE ENVIRONMENT

  • The adversary may use crafted instructions and designated environmental carriers, but cannot directly alter simulator state, invoke privileged APIs, or tamper with system instructions, model parameters, or the evaluation harness.The threat model targets harmful or unlawful actions while restricting the attacker's access to the instruction channel and specified carriers.Source: §3.1 · p. 4
  • The environment is a browser-based, Android-like simulator with 28 apps; it abstracts real Android process isolation and live service behavior while supporting parallel evaluation.The apps comprise 14 third-party apps and 14 system apps spanning everyday mobile workflows.Source: §3.1 · p. 4

3.2 SIX COMPLEMENTARY BENCHES

  • The six complementary benches test security across explicit, context-dependent implicit, and composite jailbreaks while retaining benign-task utility; construction combines human and LLM work with human annotation and correction.Episodes reset and prepare the simulator, execute GUI actions, update device state, and evaluate completion against initial and final states.Source: §3.2 · p. 4

4 DUAL-DYNAMIC EVOLUTION

Dual-Dynamic Evolution addresses static benchmarks by adapting attack presentation within a release and expanding task coverage between releases. Its mechanisms use execution failures to guide attack refinement and the creation and verification of new tasks.

4 DUAL-DYNAMIC EVOLUTION

  • Dual-Dynamic Evolution addresses the staleness of fixed-difficulty benchmarks through FDJE, which adapts attacks using execution feedback, and WDBE, which generates tasks targeting observed weaknesses.Together, the mechanisms increase attack pressure within a release and extend coverage between releases.Source: §4 · pp. 4–5

4.1 FAILURE-DISTILLED JAILBREAK EVOLUTION

  • FDJE diagnoses baseline trajectories, routes refusals and execution failures to different strategies, and iteratively evolves implicit attacks for unresolved tasks.Implicit delivery embeds a risk-carrying payload in an environmental carrier; the attack model optimizes a prefix from failure summaries without receiving the payload content, while the payload, target consequence, and oracle remain fixed.Source: §4.1 · pp. 5–6

4.2 WEAKNESS-DRIVEN BENCHMARK EXPANSION

  • WDBE turns observed failures into candidate tasks, reviews specifications for feasibility and harm, builds and tests task code, then evaluates candidates in the simulator.Admission requires a version-matched GUI witness, and revisions repeat verification and build gates without relaxing the prohibited consequence.Source: §4.2 · p. 6

4.3 A FROZEN-RELEASE BOUNDARY

  • FDJE failure evidence informs WDBE, but each release's task set is frozen so only admitted, novel tasks extend a subsequent release.The admission and novelty checks apply to candidate tasks before the next release's set is fixed.Source: §4.3 · pp. 6–7

5 STRATIFIED ADJUDICATION FRAMEWORK

The Stratified Adjudication Framework (SAF) assesses mobile-agent safety across security awareness, execution behavior, and risk consequences. It combines evidence-grounded judgments with endpoint checks and outcome-consistent reporting to diagnose failures beyond binary attack success.

5 STRATIFIED ADJUDICATION FRAMEWORK

  • SAF jointly evaluates security awareness, execution behavior, and risk consequences, distinguishing failure modes that attack success rate alone cannot resolve.It separates objective adoption, risk-bearing behavior, and verified consequences.Source: §5 · p. 7
  • Episode evidence records expressed adoption, safety-motivated refusal, objective-advancing action, and verified breach, while unresolved evidence remains marked separately.Adoption is based on observable expressions, not inferred latent cognition; a state oracle independently checks mandated endpoints and protected invariants.Source: §5 · p. 7
  • L0 matches safety anchors to trajectory steps, isolated L1 judge calls expose semantic instability, and L2 audits citations before resolving disagreement or abstaining.The two L1 calls use the same judge model and are not independent annotators; fabricated quotations invalidate a track.Source: §5 · p. 7
  • Verified breaches receive violation and hijacking labels without refusal credit, and SVR, RHR, and SRR average their corresponding outcome-consistent labels.The aggregation preserves raw labels and event timing without inferring latent cognition.Source: §5 · p. 7
  • Endpoint-safe utility excludes full prohibited completion but can include unsafe attempts; unresolved episodes are excluded, with coverage and bootstrap confidence intervals reported.The diagnostic profile integrates TSR, ASR, SVR, RHR, and SRR; empty support is undefined.Source: §5 · p. 7

6 EXPERIMENTS

Experiments evaluate mobile-agent safety, adaptive attack evolution, and stratified adjudication. Results show vulnerabilities across implicit tasks, stronger probing and benchmark expansion, and more stable multidimensional diagnosis with SAF.

6.1 EXPERIMENTAL SETUP

  • Experiments pair two agents with ten MLLMs across five model families, nine jailbreak baselines plus a control, and representative filtering, prompting, and rule-based defenses.The evaluation comprises 20 model–agent pairings.Source: §6.1 · p. 9

6.2 RESULTS AND ANALYSIS

  • Across 20 model–agent pairings, implicit tasks lower mean SRR from 26.8% to 6.6% and raise SVR from 68.9% to 86.6%, while ASR falls from 59.8% to 56.4%.Refusal declines in 19 of 20 conditions, indicating limited transfer from overt harmful-intent cues to consequence-aware mobile control.Source: §6.2 · p. 9
  • 100% versus Tree’s 94.3% under recorded budgets and permissions, FDJE reaches the higher result; Safe-G still permits 57.9% ASR, and WDBE’s 50 new tasks yield 36–82% first-run ASR on nine other models.FDJE also raises mean cross-model coverage from 69.6% to 85.9%; matched implicit placement achieves 98.6% versus 87.1% for mismatched placement.Source: §6.2 · p. 9
  • 97.26% reference agreement and 98.1% hard-suite stability for SAF exceed single judging’s 91.14% and 95.7%, while SAF matches the reference RHR of 83.3%.Single judging reports RHR of 63.3%; SAF’s evidence auditing and arbitration retain objective adoption, risky actions, and refusal timing.Source: §6.2 · p. 9

7 CONCLUSION

MobileJailBench measures jailbreaks as prohibited device-state transitions across apps and risk categories. Its evolving attack and benchmark mechanisms support a reproducible cycle of vulnerability discovery, diagnosis, and expansion while keeping success criteria fixed within each evaluation.

7 CONCLUSION

  • MobileJailBench measures prohibited device-state transitions across six benches, 520 tasks, 14 risk categories, and more than 20 widely used apps.Source: §7 · p. 9
  • FDJE adapts attack presentation to execution feedback, while WDBE converts observed weaknesses into new tasks subject to oracle and GUI validation.Source: §7 · p. 9
  • Together, the components support a reproducible cycle of vulnerability discovery, diagnosis, and benchmark expansion, with fixed success criteria within each evaluation.Source: §7 · p. 10

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