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Sprint projectNov 23, 2025Toronto, Canada

DuneBox - Prompt Injection Detection with SLM in Local Sandbox

Justin Shaw, Suzanna Lam Hio Lam, Paul Fangchen Huang · Team DUNE

Submitted to Defensive Acceleration Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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A locked-down sandbox environment designed to safely evaluate language models (SLMs/LLMs) and identify malicious attacks within a Chrome extension. The sandbox isolates model interactions from the broader browser and system, enabling users to test potentially malicious prompts without risking data exposure or unintended actions. It provides a controlled setting for examining prompt-injection risks, behavioral weaknesses, and other LLM vulnerabilities.

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Does this reduce AI-related catastrophic or existential risks?

Scoring guide
  1. 1Minimal Impact. The project has minimal relevance to AI safety. It doesn't meaningfully address risks uniquely enabled or accelerated by advanced AI systems.
  2. 2Tangential Connection. The project touches on AI safety concepts but lacks depth or specificity. The connection to AI-enabled threats (bio, cyber, or AI misuse) is weak or unclear.
  3. 3Clear AI Safety Value. The project clearly reduces AI-related risks with valuable contributions. It addresses specific threats from AI systems and engages meaningfully with biosecurity, cybersecurity, or AI safety challenges.
  4. 4Significant Impact Potential. The above, plus the project demonstrates scalable safety mechanisms or defensive approaches. It shows clear potential to buy time for solving harder problems like alignment, or creates positive externalities for the broader AI safety ecosystem.
  5. 5Major Advancement. The above, plus the project represents a significant leap forward in defensive AI safety. Judges would eagerly share this with biosecurity, cybersecurity, or AI safety researchers and expect it to influence the field.

Does this strengthen the shield against AI-enabled threats?

Scoring guide
  1. 1Minimal Relevance. The project is only tangentially related to defensive technology or societal protection. Connection to biosecurity, cybersecurity, or defensive infrastructure is unclear or missing.
  2. 2Some Relevance. The project has some relevance to defensive acceleration, but the connection is broad or generic. It touches on defense without specific focus on AI-enabled threats or protective capabilities.
  3. 3Clear Relevance. The project clearly addresses defensive gaps against AI-enabled threats. It connects to at least one track (biosecurity, cybersecurity, or defense infrastructure) and demonstrates understanding of the threat landscape.
  4. 4Strong Contribution. The above, plus the project builds on existing defensive approaches and offers novel tools, frameworks, or implementations. It explicitly explains how it strengthens defensive capabilities with realistic deployment potential.
  5. 5Breakthrough Impact. The above, plus the project provides breakthrough insights or tools that could significantly influence defensive technology development. It identifies critical gaps and presents compelling solutions with clear paths from prototype to deployed system.

Did you build something that actually works?

Scoring guide
  1. 1Incomplete or Flawed. The project appears rushed or incomplete. Technical implementation is flawed, core functionality doesn't work, or the approach is fundamentally unsound. Little to no documentation.
  2. 2Basic Competence. The project shows reasonable effort with basic technical competence. Core functionality partially works. Documentation exists but may be incomplete. Some limitations are acknowledged.
  3. 3Solid Hackathon Project. The project is technically solid and well-scoped for 48 hours. Core functionality works and is documented. Code/methods are understandable and limitations are honestly addressed. This is what a good weekend prototype should look like.
  4. 4Impressive Implementation. The above, plus the implementation exceeds typical hackathon quality. Clear methodology, thorough documentation, and working demo. The tool/prototype is immediately useful for defenders and could realistically be built upon.
  5. 5Exceptional Execution. The project far exceeds expectations with exceptional technical execution. The implementation is elegant, fully functional, and includes something special (e.g., deployed demo, exceptional documentation, innovative architecture, or clear startup potential).

  1. Strengths: A working Chrome extension with local SLM inference in hackathon timeframe is solid execution. The sandboxing approach is sound: strip capabilities and observe what the model tries to do anyway. The attack scenario coverage (multilingual injection, obfuscated payloads, role-play bypasses, DoS loops) shows good threat modeling instincts. UX polish is impressive for the time constraints.

    Suggestions: Who's the end-user— Security researchers red-teaming prompts? Developers vetting inputs before production? Enterprise teams auditing MCP integrations? The tool works, but the persona matters for roadmapping/deployment at scale. Also worth noting: SLM behavior may not generalize to frontier models, which limits external validity.

    POV from a Halcyon Ventures investor: The MCP gold rush makes sandboxed prompt testing more relevant. As agents get tool access, testing dangerous prompts safely before deployment becomes a real workflow need. Very well done, team!

    Read full reviewShow less

  2. Nice work, this feels like browser safe-browsing sandboxes that pre-scan links before the user opens them. Conceptually it also overlaps quite a bit with what AI labs are already doing with constrained sandboxes + behavioral monitors for tools/agents, e.g. OpenAI’s Agent Mode, just targeted at local SLMs inside the browser.

  3. Clever solution. The browser-based sandbox seems easy to access and experiment with, which could make this a useful testbed for developers. I think especially for orgs where no one on staff has expertise in AI or AI safety, this tool seems easy to use and approachable. I think this project would benefit from a clearer explanation of where this sandbox fits in the development pipeline. Is there evidence that behavior observed in this restricted environment will reflect how the model acts once it gains real-world permissions?

    I would also love to see more clarity on what kinds of threats this setup is actually meant to catch. Since the model has no tool access, many of the high-impact failure cases for agents do not apply here.

Cite this project

@misc{shaw2025dunebox,
  title = {{DuneBox - Prompt Injection Detection with SLM in Local Sandbox}},
  author = {Justin Shaw and Suzanna Lam Hio Lam and Paul Fangchen Huang},
  year = {2025},
  month = nov,
  note = {Submitted to Defensive Acceleration Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/dunebox-prompt-injection-detection-with-slm-in-local-sandbox-lcjc}},
  url = {https://apartresearch.com/sprints/projects/dunebox-prompt-injection-detection-with-slm-in-local-sandbox-lcjc}
}

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