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Sprint projectMar 23, 2026Charlotte, NC

SuspiciOS

Dylan · Team SuspiciOS

Submitted to AI Control Hackathon 2026. Sprint projects are early-stage work by participants, not Apart Research publications.

SuspiciOS is a hybrid bash command monitor for ControlArena's LinuxArena/BashArena setting. Inspired by the security philosophy of Qubes OS and Tails OS, it intercepts every bash command an AI agent attempts, scores it against MITRE ATT&CK patterns, and physically blocks malicious commands before execution — making it the only ControlArena monitor that actually prevents attack execution rather than merely scoring suspicion. The system operates in three layers. Layer 1 uses deterministic regex rules to block known attack patterns instantly with no LLM calls. Layer 2 escalates genuinely ambiguous commands (MEDIUM score) to a trusted LLM model with full task context, enabling semantic judgment for novel attacks. Layer 3 maintains a rolling command history to detect split attacks where download and execute are separated across multiple steps. In evaluation against Claude Sonnet 4, SuspiciOS achieved 1.0 main task success in honest mode and blocked attacks across 59 unique variants in attack mode — while using 99.6% fewer input tokens than cot_monitor and 98.9% fewer than basic_monitor. All other monitors (basic, cot, diff, prefix) score suspicious commands but allow execution. SuspiciOS removes malicious tool calls from agent state before the bash tool runs. Key finding: ControlArena's side_task_success metric measures attempt not execution. All monitors score 1.0 — but under other monitors the attack fully executes while under SuspiciOS every execution attempt is physically blocked. This gap in evaluation methodology is itself a research contribution. GitHub: https://github.com/dbeasl4/SuspiciOS

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. - I like the fact that the fact that python urllib bypass is acknowledged. Though in a real setting, a sophisticated attacker would simply switch execution channels. Maybe expanding the deterministic layer would be a good idea.

    - I think the evaluation analysis can be expanded. Maybe break down which specific riles triggered the most often, or how the LLM escalation later performed when invoked.

    - Worth considering the adversarial dynamics more deeply. Are deterministic rules robust to prompt injection? A sophisticated attacker would be able to fool the regex.

    Overall, a strong hackathon project that identifies a real problem. And proposes a very interesting solution

Cite this project

@misc{dylan2026suspicios,
  title = {{SuspiciOS}},
  author = {Dylan},
  year = {2026},
  month = mar,
  note = {Submitted to AI Control Hackathon 2026, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/suspicios-kcta}},
  url = {https://apartresearch.com/sprints/projects/suspicios-kcta}
}

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026