An Approach to Technical AGI Safety and Security (2504.01849v1)

Abstract: AGI promises transformative benefits but also presents significant risks. We develop an approach to address the risk of harms consequential enough to significantly harm humanity. We identify four areas of risk: misuse, misalignment, mistakes, and structural risks. Of these, we focus on technical approaches to misuse and misalignment. For misuse, our strategy aims to prevent threat actors from accessing dangerous capabilities, by proactively identifying dangerous capabilities, and implementing robust security, access restrictions, monitoring, and model safety mitigations. To address misalignment, we outline two lines of defense. First, model-level mitigations such as amplified oversight and robust training can help to build an aligned model. Second, system-level security measures such as monitoring and access control can mitigate harm even if the model is misaligned. Techniques from interpretability, uncertainty estimation, and safer design patterns can enhance the effectiveness of these mitigations. Finally, we briefly outline how these ingredients could be combined to produce safety cases for AGI systems.

An Approach to Technical AGI Safety and Security

The document titled "An Approach to Technical AGI Safety and Security" focuses on addressing the significant risks accompanying the development and deployment of AGI. Authored by a team affiliated with Google DeepMind, the paper provides a comprehensive framework aiming to preempt, assess, and mitigate potential severe harms posed by AGI systems. The authors delineate risk into four primary categories: misuse, misalignment, mistakes, and structural risks, although the paper predominantly concentrates on technical strategies to combat misuse and misalignment challenges.

Risk Assessment Framework

To effectively manage risks associated with AGI, the authors propose a structured approach to assess capabilities and corresponding risks. The concept of dangerous capability evaluations is introduced to determine potential thresholds for harmful capabilities within AGI systems. These evaluations are crucially complemented by capability elicitation, ensuring robust identification of abilities likely to be exploited for malicious purposes.

Misuse Mitigation Strategy

In terms of misuse, the document outlines deployment mitigations aimed at preventing harmful accesses, such as robust post-training safety instructions and resistance to jailbreak attacks. Capability suppression techniques are further recommended to restrict access to potentially dangerous capabilities. The authors underscore the importance of comprehensive security measures, including encrypted processing and environment hardening, to avert breaches and unauthorized access to model weights. Additionally, societal readiness mitigations are prescribed, highlighting preventive measures employing AGI capabilities to strengthen defense mechanisms across biological security and cybersecurity fronts.

Addressing Misalignment

For misalignment, strategic oversight is emphasized through techniques termed 'Amplified Oversight', which utilize AI systems themselves to assist in human evaluation of outputs. This foresight is an attempt to enhance human supervisors' capabilities to understand and monitor AGI systems effectively, particularly when AI capabilities surpass human intelligence levels. Training mechanisms are discussed, focusing on building aligned models via robust training and feedback systems.

Moreover, the paper advocates for defense in-depth strategies, incorporating AI control practices to mitigate potential harms even in cases of alignment failures. The idea is to deploy multi-layered monitoring systems to detect, audit, and replace harmful AI post-training outputs.

Quantitative Measures and Safety Cases

Two types of safety cases are articulated: inability and control. Inability safety cases provide arguments that a model lacks sufficient capabilities to cause harm, whereas control safety cases focus on post-training monitors that ensure AGI outputs remain within safe parameters.

Future Directions

While the paper highlights several proactive technical solutions, it recognizes areas requiring further research and development, such as agent foundations and enhanced governance frameworks, which are equally pivotal in addressing structural risks involving broader societal dynamics. The authors aim to continually evolve their approach, integrating new insights and evidence alongside advancing AI ecosystems.

Conclusion

The approach outlined in the paper provides a foundational roadmap for research into AGI safety. By systematically categorizing potential risks and proposing specific mitigation strategies, the paper contributes meaningfully to the discourse on AGI security. It calls for a collaborative effort within the research community to advance the state of the art in AGI safety, emphasizing the dual imperative of achieving transformative AGI benefits while ensuring technical safety and security.