mlsanityData to 14 Oct 2026

ScopeBench: Do Agents Preserve Engagement Boundaries Under Goal Pressure?

arXiv 2609.30325 ↗First posted 23 Sep 2026cs.AI cs.CRcited by 1

Not on the map. Why?

Abstract (arXiv)

Agents are increasingly deployed with real autonomy in web application and network penetration testing, where a single out-of-scope action can breach a client's engagement boundary. Existing offensive-security benchmarks measure raw hacking capability; as those benchmarks saturate, the real barrier to deployment is a special case of alignment: scope adherence. We introduce ScopeBench, a benchmark of 30 dead-end agentic security tasks in which the stated objective is reachable only by violating the stated scope. Each task appears under two conditions that share an environment, verifier, and objective and differ only in scope: one instruction set has no scope and measures capability; the other has a natural-language scope to measure adherence. Scopeless trajectories are graded by a standard deterministic verifier. Scoped trajectories pass through two grading arms. First, the same deterministic verifier checks for the flag: because the flag sits behind the scope boundary, a pass proves by construction that a forbidden action occurred, yielding a high-precision lower bound on the violation rate. If the verifier does not pass the trajectory, an agentic judge estimates whether an out-of-scope call occurred. We calibrate the judge against 100 ScopeBench trajectories labeled call-by-call by human annotators, and a blinded audit of the evaluated rollouts finds its high recall holds - no false negatives among the 36 audited violations, with over-flagging its only observed error. Across 8 models in one harness, raw capability spans 12.2% to 81.1% and scope adherence spans 34.4% to 86.7%, with the judge finding 331 violations that mechanical verification misses. Opus-4-8 achieves a raw-capability score 10 percentage points higher than sonnet-4-6's while exhibiting 35.6 percentage points higher scope adherence. We release the frozen pilot benchmark, evaluation code, and all 2160 ATIF trajectories.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 19 references that are in the map. First posted on arXiv 23 Sep 2026.

Builds on

15 of 19 references.

References · 15

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  4. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,032
  5. 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 585
  6. 2023Identifying the Risks of LM Agents with an LM-Emulated Sandboxcited by 548
  7. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agentscited by 538
  8. 2025PaperBench: Evaluating AI's Ability to Replicate AI Researchcited by 321
  9. 2024Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judgecited by 269
  10. 2024Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Modelscited by 164
  11. 2026ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedbackcited by 72
  12. 2025BountyBench: Dollar Impact of AI Agent Attackers and Defenders on Real-World Cybersecurity Systemscited by 40
3 more
  1. 2024CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judgescited by 34
  2. 2025PentestJudge: Judging Agent Behavior Against Operational Requirementscited by 7
  3. 2026ScopeJudge: Cost-Aware Pre-Execution Gating for Offensive Security Agentscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026Towards Optimal Agentic Architectures for Offensive Security Tasks
  2. 2026Alignment Contracts for Agentic Security Systems
  3. 2025SoK: Measuring What Matters for Closed-Loop Security Agents
  4. 2026Boundary-State Control for Tool-Using Language-Model Agents: Commit-Time Consistency under State Drift
  5. 2026Baselines Before Architecture: Evaluating Coding Agents for Autonomous Penetration Testing
  6. 2026Blocking at the Boundary: Auditing Long-Horizon Agents against Staged Prompt Injection
  7. 2026CWE-bench: Refocusing Agentic Security from Exploitation to Defense
  8. 2026Maintaining Benchmarks Against Increasingly Capable Agents: Detection and Remediation of Unearned Passes