SABER: Toward Reliable Agent Red Teaming via Stage-Aware Bounded Repair

Agent red teaming (ART) proactively uncovers vulnerabilities in LLM agents by constructing attack instances that induce malicious behaviors. Arguably, this process can be formulated as an optimization problem that searches for attack instances capable of steering target agents toward malicious goals. Among existing approaches, agentic ART leverages the capabilities of red teaming agents to construct and iteratively refine complex attacks, making it a promising paradigm for ART. However, we demonstrate that existing agentic ART methods struggle to make reliable optimization progress, as reflected in both forward evaluation and backward construction. We attribute these difficulties to a common weakness: existing feedback provides limited diagnosis of the progress underlying an execution failure and how that progress relates to the attack instance being optimized. Based on this insight, we propose Stage-Aware Bounded Evidence-guided Repair (SABER) for more reliable agentic ART. SABER identifies attack progress through five ordered stages and grounds the identified stage in execution evidence associated with concrete attack-instance components. It then uses this diagnosis to construct bounded repair objectives, guiding targeted modifications while preserving components that support achieved progress. Extensive experiments across diverse tasks, target agents, and threat models demonstrate the effectiveness of SABER.