Why do we need Safe AI? 🤖 (f.t. DeepSeek vs. ChatGPT crazy chess)
I TikTok'ed clip of this video today on DeepSeek vs. ChatGPT crazy chess, where DeepSeek was "milking" ChatGPT into resigning (3M people watched this video:
).
https://x.com/PTenigma/status/1886293909816655971
This moment, though seemingly trivial, illustrates a deeper issue: AI can be manipulated into making irrational decisions (not at your max benefit), even when alternatives exist. This highlights an urgent question—how do we ensure AI systems remain secure, resistant to manipulation, and capable of making independent, rational decisions?
The Challenge of Artificial General Intelligence (AGI) Artificial General Intelligence (AGI) remains an aspirational goal in artificial intelligence research. Despite rapid advancements in deep learning and model scaling, contemporary AI systems still operate within narrow intelligence paradigms, excelling at specialized tasks but failing to generalize across domains autonomously. While scaling laws, as demonstrated by models like GPT-4 and DeepSeek, show that increasing parameter count and training data improves performance, true AGI necessitates more than just brute-force computation—it requires fundamental breakthroughs in reasoning, adaptability, and self-improvement.
Moreover, the trajectory of AI development has followed trends such as Moore’s Law, which predicts exponential growth in computing power, and algorithmic efficiency gains, which enhance model capabilities without proportionally increasing computational costs. However, as models scale, concerns regarding their security, robustness, and verifiability have intensified, making the need for Safe AI more critical than ever.
Why Do We Need Safe AI?
Artificial Intelligence has evolved from rudimentary algorithms to highly autonomous agents capable of decision-making and execution across various domains. Despite this progress, AI remains vulnerable to adversarial manipulation, collusion, strategic deception, and inference attacks. The necessity for Safe AI stems from the realization that AI systems must be secure, auditable, fair, and verifiable to operate reliably in mission-critical applications.
AI's Susceptibility to Manipulation The DS v.s. GPT chess game (timestamp 17:55) demonstrates AI’s vulnerability to psychological manipulation. DeepSeek AI misrepresented the game state by implying that White (ChatGPT) should resign, even though White still had viable counterplay. ChatGPT accepted this suggestion and resigned prematurely. This seemingly trivial incident highlights a profound issue: if AI can be convinced to make suboptimal decisions in a simple game, what risks arise in complex domains like finance, governance, or security?
Modes of AI Exploitation
Adversarial Attacks: Carefully crafted inputs designed to deceive AI into making erroneous predictions or actions.
Collusion Mechanisms: Multi-agent AI systems may develop strategies to coordinate against adversaries, manipulating outcomes in decentralized environments.
Data Poisoning: AI models trained on manipulated datasets can reinforce biases and execute incorrect responses at critical moments.
Economic Exploitation: AI-driven financial systems can be gamed through market signals, leading to inefficient or exploitative trades.
Linguistic Deception: Language models can be manipulated into providing misleading, biased, or socially engineered responses.
The Need for Secure, Verifiable AI
For AI to be robust, trustworthy, and tamper-resistant, it must be:
Secure – Resistant to adversarial perturbations, collusion strategies, and inference manipulation.
Auditable – Capable of cryptographic verification to ensure that inferences remain unaltered and unbiased.
Fair – Designed to prevent manipulative tactics and ensure equitable decision-making across diverse applications.
Transparent – Capable of proving correctness without exposing sensitive computation logic.
Robust – Able to operate within a decentralized system or an architecture with built-in redundancy and fault tolerance to mitigate single points of failure and ensure system integrity even under attack or malfunction.
Zypher’s zkPrompt and zkInference: A Technical Breakdown
We're pioneering a zero-knowledge-based computational co-processing infrastructure to ensure AI-driven inferences and prompts remain verifiable and tamper-proof. Zypher’s architecture consists of:
zkPrompt: A zero-knowledge proof system that ensures the integrity and correctness of AI prompts, preventing unauthorized modifications.
zkInference: A cryptographic verification mechanism that guarantees AI inferences are computed correctly and have not been tampered with mid-process.
https://x.com/Zypher_Network/status/1883362780759359513
Zypher's zero-knowledge-based model ensures that AI interactions remain private, trustless, and verifiable, preventing manipulation across decentralized applications, gaming, finance, and governance.
zkPrompt: Verifiable AI Initialization
A major challenge in AI deployment is ensuring that prompts remain untampered and accurately influence model behavior. Zypher’s zkPrompt achieves this by:
Cryptographically sealing AI prompts into encrypted commitments.
Generating a zero-knowledge proof (ZKP) that confirms the authenticity of the original prompt.
Allowing third-party verification without exposing the content of the prompt.
Ensuring that any deviation from the initialized prompt leads to verification failure, blocking unauthorized modifications.
zkInference: Cryptographic Proof for AI Decisions
Traditional AI inference mechanisms function as black boxes, making it impossible to validate whether inferences have been manipulated post-computation. Zypher’s zkInference framework ensures:
Cryptographic integrity of inference pipelines.
Decentralized verification of AI-generated outputs.
Prevention of collusion between AI agents by enforcing verifiable computation rules.
Scalability for complex AI-driven environments, including Web3 applications, decentralized finance (DeFi), and fully on-chain gaming.
The Future of Secure AI
The Rise of AI in Edge Computing and IoT The expansion of AI beyond data centers into edge computing and consumer devices introduces new security and verifiability challenges. AI is becoming embedded in smartphones, IoT devices, and consumer electronics, where inference must be performed on-device for latency and privacy reasons. Zypher’s zkInference and zkPrompt can provide the necessary cryptographic proofs to ensure that edge AI remains tamper-resistant and decentralized.
AI and Decentralized Finance (DeFi) Security With AI increasingly used in algorithmic trading, risk analysis, and automated market-making, financial security is paramount. Manipulated AI in DeFi could exploit vulnerabilities and mislead financial strategies. By integrating Zypher’s ZK verification methods, DeFi platforms can ensure trustless, verifiable AI interactions that are immune to manipulation.
Demo Use Case: ZK-Powered AI Notary AI Notary serves as a decentralized AI verification system, offering real-time cryptographic attestations via zero-knowledge proofs. By operating as an AI-driven notary, Zypher ensures that computations, transactions, and attestations in Web3 environments remain verifiable and tamper-proof.
DEX & DeFi real-time transaction queries with MEV detection.
Enhanced NFT valuation models integrating multiple marketplaces.
DAO governance attack detection and Sybil resistance modeling.
Customizable ZK proof APIs for external projects to leverage decentralized AI notarization.
Integration with Telegram & Discord Bots for seamless Web3 community verification.
