Global Challenge for Safe and Secure LLMs Track 1
On the map: LLM safety and security → Prompt injection and agent security
Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security. Builds on 14 of its 21 references that are in the map. First posted on arXiv 21 Nov 2024.
Builds on
14 of 21 references.
Closest ancestors · 1
Same direction, newest first
Other references · 13
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
- 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
- 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,651
- 2023Jailbreaking Black Box Large Language Models in Twenty Queriescited by 1,420
- 2023Jailbreaking ChatGPT via Prompt Engineering: An Empirical Studycited by 498
- 2024Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attackscited by 486
- 2024SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decodingcited by 247
- 2023Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attackscited by 194
- 2023Open Sesame! Universal Black Box Jailbreaking of Large Language Modelscited by 134
- 2024Improved Techniques for Optimization-Based Jailbreaking on Large Language Modelscited by 110
- 2024Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstructioncited by 107
- 2024Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?cited by 34
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2024Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Modelscited by 33
- 2025Safety Alignment Should Be Made More Than Just A Few Attention Headscited by 5
- 2026Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategiescited by 2
- 2026Trustworthy 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 AIcited by 0
- 2026Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies8 Jul 2026
- 2026Trustworthy 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 AI22 Feb 2026
- 2025Safety Alignment Should Be Made More Than Just A Few Attention Heads27 Aug 2025
- 2024Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models8 Dec 2024
Similar, not cited
Close in wording, no citation either way
- 2025Dark LLMs: The Growing Threat of Unaligned AI Models
- 2025Evolving Security in LLMs: A Study of Jailbreak Attacks and Defenses
- 2024Exploring Vulnerabilities and Protections in Large Language Models: A Survey
- 2025Safety and Security Analysis of Large Language Models: Benchmarking Risk Profile and Harm Potential
- 2024Security and Privacy Challenges of Large Language Models: A Survey
- 2025Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
- 2024Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes
- 2025A Survey of Attacks on Large Language Models