I previously brought attention to Sentient in the blog post ‘Sentient’s Loyal AI Resolves AGI Ethical Risks’, in which I highlighted Sentient’s risk mitigation strategies – including robust AI alignment framework development, transparent and explainable AI system implementation, and rigorous testing of community-first safety protocols – as solutions to various ethical risks associated with AGI – such as bias, manipulation, weaponization, and more. However, Sentient’s risk mitigation strategies do more than enable a “Loyal AI” that controls for existential threats and misalignment of AI systems from human values – they also create robust defense against AI supply chain attacks, one of the most pervasive, persistent, and rapidly evolving cybersecurity threats of our time.
The AI supply chain attack surface is broad and weakly defended. For example, supply chain attacks can hijack autonomous agents in order to redirect workflows and business logic – a case especially important in the world of Web3. Sentient stressed the magnitude of this risk in the paper ‘AI Agents in Cryptoland: Practical Attacks and No Silver Bullet’: “The integration of AI agents with Web3 ecosystems harnesses their complementary potential for autonomy and openness, yet also introduces underexplored security risks, as these agents dynamically interact with financial protocols and immutable smart contracts.” In this paper, Sentient introduced a supply chain attack involving action space exploitation, context manipulation, and malicious model deployment, and demonstrated how adversaries can cause ElizaOS, a decentralized Al agent framework for automated Web operations, to violate its protocols and unintentionally transfer assets.
Not only is the AI supply chain attack surface vast, but the AI supply chain is complex and opaque, as well, making it inherently difficult to secure.
At the top level, Sentient solves this problem through blockchain provenance and cryptographic signing. These methods ensure transparent traceability of models and updates, prevent cross-contamination from third-party tools and data poisoning before training begins, allow for verification of model and code authenticity, enable real-time anomaly detection, and create a Zero Trust architecture for the verification of every component in the AI supply chain. In addition, Sentient mitigates AI supply chain attack risks by building blockchain resiliency into every layer of its AI system – including storage, distribution, access, and incentives. Not only that, but through a novel approach to model “fingerprinting”, Sentient’s “AI native cryptography” transforms well-known security threats and backdoor attacks into a security tool for protecting long-term model sustainability and defending against supply chain exploitation and misuse – at the same time.
Fingerprinting is a groundbreaking technology that addresses a critical gap in AI supply chain transparency and accountability. Sentient’s advanced fingerprinting technique facilitates proof of model ownership, allows communities to align models with set values, and ensures that any updates and changes to a model are communicated and authenticated before deployment – preserving the integrity and continuity of supply chain governance. Making model origin, intention, and usage transparent, and characterized as collusion resistant, harmless, persistent, resilient, and scalable, Sentient’s fingerprints are designed to allow AI model owners to quickly and easily discover misuse, theft, and unauthorized modifications in real time.
Sentient clearly believes that innovation in AI must not come at the expense of trust and is pioneering a new era in AI: not only is Sentient empowering communities to create AI that is community-built, community-aligned, and community-owned, but Sentient is also securing the AI supply chain through its novel fingerprinting technique so that we can safely, ethically, and resiliently unlock the full potential of generative AI.
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