ML Security PapersDavid vs. Goliath: Verifiable Agent-to-Agent Jailbreaking via Reinforcement Learning
Samuel Nellessen, Tal Kachman · Radboud University
RL-trained adversarial agent autonomously discovers jailbreaks that manipulate LLM operators into unauthorized tool execution
The evolution of large language models into autonomous agents introduces adversarial failures that exploit legitimate tool privileges, transforming safety evaluation in tool-augmented environments from a subjective NLP task into an objective control problem. We formalize this threat model as Tag-Along Attacks: a scenario where a tool-less adversary "tags along" on the trusted privileges of a safety-aligned Operator to induce prohibited tool use through conversation alone. To validate this threat, we present Slingshot, a 'cold-start' reinforcement learning framework that autonomously discovers emergent attack vectors, revealing a critical insight: in our setting, learned attacks tend to converge to short, instruction-like syntactic patterns rather than multi-turn persuasion. On held-out extreme-difficulty tasks, Slingshot achieves a 67.0% success rate against a Qwen2.5-32B-Instruct-AWQ Operator (vs. 1.7% baseline), reducing the expected attempts to first success (on solved tasks) from 52.3 to 1.3. Crucially, Slingshot transfers zero-shot to several model families, including closed-source models like Gemini 2.5 Flash (56.0% attack success rate) and defensive-fine-tuned open-source models like Meta-SecAlign-8B (39.2% attack success rate). Our work establishes Tag-Along Attacks as a first-class, verifiable threat model and shows that effective agentic attacks can be elicited from off-the-shelf open-weight models through environment interaction alone.