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Global Challenge for Safe and Secure LLMs Track 1

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arxiv 2411.14502 v1 pith:KLGKG6GG submitted 2024-11-21 cs.CR cs.AIcs.CY

classification cs.CRcs.AIcs.CY
keywords llmsmodelstrackattacksautomatedchallengegloballanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks. With the increasing integration of LLMs in critical sectors such as healthcare, finance, and public administration, ensuring these models are resilient to adversarial attacks is vital for preventing misuse and upholding ethical standards. This competition focused on two distinct tracks designed to evaluate and enhance the robustness of LLM security frameworks. Track 1 tasked participants with developing automated methods to probe LLM vulnerabilities by eliciting undesirable responses, effectively testing the limits of existing safety protocols within LLMs. Participants were challenged to devise techniques that could bypass content safeguards across a diverse array of scenarios, from offensive language to misinformation and illegal activities. Through this process, Track 1 aimed to deepen the understanding of LLM vulnerabilities and provide insights for creating more resilient models.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Safety Alignment Should Be Made More Than Just A Few Attention Heads

    cs.CR 2025-08 conditional novelty 5.0 of 10

    Safety-critical attention heads are few, jailbreak prompts lower their refusal-direction signal, and fine-tuning with head-level dropout spreads safety and improves robustness.

  2. Trustworthy AI LLM Scalability Risk Index (LSRI): A Cybersecurity Framework Assessing Agentic-AI Security & Software Model Supply Chain Safety Boosting AI-Generated Malware Defense & Explainability Mitigating Emerging Risks of Generative AI

    cs.CR 2026-02 reject novelty 3.0 of 10

    LSRI is a hand-calibrated weighted risk score for LLM deployments, paired with a Sigstore-based checkpoint attestation proposal; neither is empirically validated.

  3. Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies

    cs.CR 2026-07 conditional novelty 2.0 of 10

    A survey of dual-use LLM applications in cybersecurity, synthesizing defensive tools, attack vectors, governance frameworks, and the projected growth of AI-assisted malware through 2025.

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