The ARES framework targets systemic vulnerabilities where both the LLM and its reward model fail to detect unsafe behavior. It uses a "Safety Mentor" to compose adversarial prompts based on specific personas and tactics. This approach repairs the underlying policy-reward system rather than just patching individual prompts, improving alignment robustness for practitioners.