mlsanityData to 14 Oct 2026

LinuxArena: A Control Setting for AI Agents in Live Production Software Environments

arXiv 2604.15384 ↗First posted 16 Apr 2026cs.CR cs.AI cs.SEcited by 8

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

We introduce LinuxArena, a control setting in which agents operate directly on live, multi-service production environments. LinuxArena contains 20 environments, 1,671 main tasks representing legitimate software engineering work, and 184 side tasks representing safety failures such as data exfiltration and backdooring, making it the largest and most diverse control setting for software engineering to date. We validate LinuxArena is useful for control research by running sabotage evaluations, which measure whether attackers can complete side tasks while working on main tasks, and monitor evaluations, which measure a monitor model's ability to detect sabotage attempts. Against a GPT-5-nano trusted monitor at a 1% step-wise false positive rate, Claude Opus 4.6 achieves roughly a 23% undetected sabotage success rate. We additionally release LaStraj, a dataset of human-crafted attack trajectories that evade monitors at substantially higher rates than any model-generated attacks we elicited, showing that current attack policies do not saturate LinuxArena. These results suggest that LinuxArena has meaningful headroom for both attackers and defenders, making it a strong testbed for developing and evaluating future control protocols.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 13 of its 24 references that are in the map. First posted on arXiv 16 Apr 2026.

Builds on

13 of 24 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025BashArena: A Control Setting for Highly Privileged AI Agents17 Dec
  2. 2025Async Control: Stress-testing Asynchronous Control Measures for LLM Agents15 Dec
  3. 2025SHADE-Arena: Evaluating Sabotage and Monitoring in LLM Agents17 Jun
  4. 2025How to evaluate control measures for LLM agents? A trajectory from today to superintelligence7 Apr

Other references · 9

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  3. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
  4. 2024Alignment faking in large language modelscited by 360
  5. 2023AI Control: Improving Safety Despite Intentional Subversioncited by 229
  6. 2023Scheming AIs: Will AIs fake alignment during training in order to get power?cited by 97
  7. 2024Sabotage Evaluations for Frontier Modelscited by 52
  8. 2025Ctrl-Z: Controlling AI Agents via Resamplingcited by 44
  9. 2024Games for AI Control: Models of Safety Evaluations of AI Deployment Protocolscited by 31

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 8.

8 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026CWE-bench: Refocusing Agentic Security from Exploitation to Defense
  2. 2026Attack Selection in Agentic AI Control Evaluations Meaningfully Decreases Safety
  3. 2025DoomArena: A framework for Testing AI Agents Against Evolving Security Threats
  4. 2026ProtoArena: Can AI Agents Discover and Exploit Real-World Cross-Component Protocol Vulnerabilities?
  5. 2026PrivEscalate: Measuring and Augmenting the Threat of LLM-Automated Linux Privilege Escalation ↗
  6. 2026AgentXploit: Autonomous Repository-to-Runtime Red-Teaming for AI Agents
  7. 2026CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents
  8. 2026Bootstrapped Monitoring: Leveraging Transparent Reasoning to Oversee Stronger AI Agents