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Sprint projectNov 23, 2025

GUARDIAN: Guarded Universal Architecture for Defensive Interpretation And traNslation

Aditya Thalang, Josh Brown · Team Guardian team

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

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Report: GUARDIAN: Guarded Universal Architecture for Defensive Interpretation And traNslation

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GUARDIAN is a multi-stage, LLM-driven system to automate the translation of C codebases to memory-safe Rust. GUARDIAN promotes defense acceleration at-scale by guiding an LLM transpiler with dependency graph strongly-connected-components, static-analysis-guided rule hints, examples from the demonstration corpora and iterative, compiler-guided refinement. In evaluation on 27 C functions, including 20 with adversarial vulnerabilities, GUARDIAN achieves 100% compilation success and 92.6% fully safe outputs, outperforming a baseline LLM by 22.2pp. GUARDIAN demonstrates that safety-oriented constraints can significantly improve automated translation quality at scale. Limitations include evaluation on a small test set and a lack of functional-equivalence guarantees; future work will target repository-scale evaluation, expanding the classes of vulnerabilities covered by static analyses, adding functional-equivalence guarantees and robust evaluation sandboxing.

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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: This addresses a real defensive bottleneck. Memory-unsafe code dominates vulnerability lists, and manual C-to-Rust migration is slow. The multi-stage pipeline is well-designed. The 22pp improvement over baseline LLM on adversarial cases is meaningful. Clear alignment with CISA/White House guidance on memory-safe languages.

    Suggestions: The test suite is small and compilation success may not guarantee semantic equivalence. The system could produce memory-safe code that behaves differently than the original. Functional equivalence testing (unit test generation, property-based testing) would strengthen confidence. Red-teaming with adversarially crafted C code that exploits the pipeline's assumptions would also help.

    From a Halcyon Ventures investor's POV: This maps to our interest in securing AI infrastructure and reducing attack surface in legacy systems. We are also interested in novel solutions for remediating open-source code vulnerabilities at scale, particularly as open-source AI development rises globally. This was an impressive effort and badly needed, well done!

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  2. Well-motivated project with a clear defensive use case. The report explains the problem and deployment path well, and the prototype results are encouraging. My main concerns are robustness and real-world reliability. I'd like to see a better argument for the broader qualitative impact of this project.

Cite this project

@misc{thalang2025guardian,
  title = {{GUARDIAN: Guarded Universal Architecture for Defensive Interpretation And traNslation}},
  author = {Aditya Thalang and Josh Brown},
  year = {2025},
  month = nov,
  note = {Submitted to Defensive Acceleration Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/guardian-guarded-universal-architecture-for-defensive-interpretation-and-translation-vqgy}},
  url = {https://apartresearch.com/sprints/projects/guardian-guarded-universal-architecture-for-defensive-interpretation-and-translation-vqgy}
}

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