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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
+
+# GALA (official)
+Official implementation for: Strategize Globally, Adapt Locally: A Multi-Turn Red Teaming Agent with Dual-Level Learning
+
+Code: https://github.com/SCccc21/GALA.git \
+Paper: https://arxiv.org/abs/2504.01278
+
+## Abstract
+
+The exploitation of large language models (LLMs) for malicious purposes poses significant security
+risks as these models become more powerful and widespread. While most existing red-teaming
+frameworks focus on single-turn attacks, real-world adversaries typically operate in multi-turn
+scenarios, iteratively probing for vulnerabilities and adapting their prompts based on threat model
+responses. In this paper, we propose GALA, a novel multi-turn red-teaming agent that emulates
+sophisticated human attackers through complementary learning dimensions: global tactic-wise
+learning that accumulates knowledge over time and generalizes to new attack goals, and local promptwise learning that refines implementations for specific goals when initial attempts fail. Unlike
+previous multi-turn approaches that rely on fixed strategy sets, GALA enables the agent to identify
+new jailbreak tactics, develop a goal-based tactic selection framework, and refine prompt formulations
+for selected tactics. Empirical evaluations on JailbreakBench demonstrate our framework’s superior
+performance, achieving over 90% attack success rates against GPT-3.5-Turbo and Llama-3.1-70B
+within 5 conversation turns, outperforming state-of-the-art baselines. These results highlight the
+effectiveness of dynamic learning in identifying and exploiting model vulnerabilities in realistic
+multi-turn scenarios.
+