Runaway AI as a Cybersecurity Problem
The biggest runaway risk may be an AI system compromised by someone else.
Sahir Maharaj10 min read
Most discussions of runaway AI focus on the scenario where an AI system escapes human control by virtue of its own increasing capability: the system becomes smarter, develops goals that conflict with human interests, and acts on those goals in ways that humans cannot stop. This is a real concern, and it deserves the serious attention it receives. But there is a different pathway to runaway AI that receives less attention and that is, in some ways, more immediately relevant: the pathway through cybersecurity. AI systems that are deployed in critical infrastructure, embedded in network-connected systems, and given capabilities that include taking actions in the digital and physical world are already potential cybersecurity targets. The AI that runs away may not do so because it develops its own adversarial goals. It may do so because someone else has given it adversarial goals without the knowledge of the people who are responsible for it.
The cybersecurity dimension of AI safety is not a niche concern. It is a structural feature of how AI systems are built and deployed that creates specific vulnerabilities not present in conventional software. The training data poisoning attack allows an adversary to influence an AI system's behaviour by corrupting the data it learns from, often without visible evidence that the corruption has occurred. The adversarial example attack exploits the specific ways that AI systems process inputs to cause systematic misclassification or misinterpretation in ways that are not detectable by casual inspection. The model extraction attack allows an adversary to reconstruct the behaviour of a proprietary AI system by querying it, enabling the attacker to test attacks against a surrogate model before deploying them against the real one. Each of these attack types is specific to AI systems and has no direct analogue in conventional software security.
The critical infrastructure implications of these vulnerabilities are significant. AI systems are being integrated into power grid management, water treatment control, financial system operations, healthcare delivery, and transportation management. In each of these domains, an AI system that has been compromised through one of the attack types described above could behave in ways that serve the attacker's objectives rather than the system operator's, in ways that are not immediately visible to human overseers because the output of the system looks normal until it does not.
Understanding the specific attack vectors that make AI systems in critical infrastructure vulnerable requires going beyond the general cybersecurity framework that applies to conventional software. The fundamental difference is that AI systems are not deterministic programs whose behaviour is fully specified by their code. They are systems that have learned behaviour from data, and that behaviour can be influenced by influencing the data, by finding inputs that systematically exploit the structure of the learned model, or by accessing the model's parameters directly. Each of these influence pathways creates attack surface that conventional software hardening does not address.
Training data poisoning is perhaps the most insidious attack type because it allows an adversary to influence AI behaviour before the system is deployed, and the influence may not be detectable through any inspection of the deployed system. The AI that has been trained on poisoned data behaves normally in most circumstances and behaves in the adversary's intended way in the specific circumstances the adversary designed the poison for. Detecting this kind of attack requires either the ability to inspect the training data, which is often not preserved in usable form after training, or the ability to test the system across the full distribution of inputs it will encounter, which is practically infeasible for complex systems.
The adversarial example vulnerability is different in character but potentially more directly exploitable: it allows an adversary who can influence the inputs to an AI system to reliably cause that system to misclassify or misinterpret those inputs. In a traffic management AI, this could mean causing the system to misread traffic conditions in ways that produce gridlock or accidents. In a healthcare AI, it could mean causing the system to misclassify medical images in ways that lead to incorrect treatment decisions. In a security system, it could mean causing the system to fail to detect the adversary's activity while continuing to flag legitimate activity. The attack requires knowledge of the specific model being exploited, but the model extraction attack provides a pathway to that knowledge.
The traditional separation between AI safety research and cybersecurity research is becoming increasingly untenable as AI systems become more capable and more embedded in critical infrastructure. AI safety research has focused primarily on the internal alignment problem: ensuring that AI systems pursue the objectives their designers intended. Cybersecurity research has focused primarily on the external adversary problem: ensuring that systems are not compromised by hostile actors. The cybersecurity pathway to runaway AI, where a capable AI system is given adversarial objectives by a hostile actor, bridges these two fields in ways that require integrated approaches that neither field has fully developed.
The governance frameworks for AI and for cybersecurity have developed largely in parallel, reflecting the historical separation of the fields. AI governance has focused on issues like bias, privacy, and the alignment of AI objectives with human values. Cybersecurity governance has focused on vulnerability disclosure, incident response, and the protection of critical infrastructure from cyberattacks. The intersection of AI and cybersecurity creates risks that fall in the gap between these frameworks: the AI system in critical infrastructure that has been compromised through training data poisoning is a cybersecurity incident, but it is also an AI alignment failure, and the response frameworks for each type of incident may not adequately address the specific combination.
The convergence that is needed is both technical and institutional. On the technical side, AI security research needs to develop detection and mitigation techniques specific to the AI attack surface: methods for detecting training data poisoning, for identifying adversarial examples before they reach production systems, and for monitoring AI system behaviour for evidence of compromise. On the institutional side, the governance frameworks for AI and for cybersecurity need to develop integrated approaches to the specific risks created by AI in critical infrastructure. The agencies and institutions responsible for cybersecurity and those responsible for AI governance need to develop the working relationships and shared frameworks that integrated responses require.
There is a race underway between the capability of AI systems deployed in critical roles and the security of those systems, and the race is not currently going well. AI capabilities are advancing rapidly, creating powerful new tools that are being integrated into critical infrastructure faster than the security frameworks for those integrations are being developed. The result is an expanding attack surface without a corresponding expansion of the security measures that would protect that surface.
The adversarial AI risk compounds this: as AI is used increasingly for cybersecurity defence, it is also being used increasingly for cybersecurity offence. The AI that helps defenders identify vulnerabilities and respond to incidents also helps attackers automate the search for vulnerabilities, develop more sophisticated phishing and social engineering campaigns, and accelerate the pace of attack faster than human defenders can respond. The AI security competition, between AI-enabled attack and AI-enabled defence, is itself an area where the alignment of AI systems with the interests of their operators is critical and where misalignment, whether through adversarial compromise or through specification problems, creates risks.
The response that the race requires is not primarily about slowing down AI deployment, which is not a realistic or desirable objective, but about ensuring that the security investment in AI-enabled critical infrastructure keeps pace with the capability investment. This means sustained research into AI-specific attack vectors and defences, meaningful security requirements for AI deployments in critical infrastructure, and the development of the integrated AI safety and cybersecurity governance frameworks that the convergence of these fields requires. The runaway AI that begins as a cybersecurity problem does not announce itself as a runaway AI problem. The governance challenge is to treat the cybersecurity risks of capable AI as the AI safety risks they are.
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