How AI is Transforming Crypto and DeFi — And Why Security Matters
As AI starts blending with blockchain and DeFi, we’re seeing some exciting possibilities: trading bots that learn on the fly, systems that adjust risk automatically, and even more tailored financial services. But specialists also point out that these AI-driven DeFi systems could bring new kinds of risks, so they need to be studied and tested thoroughly.
Jason Jiang, CBO of CertiK, notes in the The intersection of DeFi and AI calls for transparent security article that:
Every line of AI logic is still code, and every line of code can be exploited.
In other words, accelerated AI integration must be matched by accelerated security measures.
Key Takeaways:
- AI is bringing new power to DeFi, offering advanced tools for fraud detection and risk monitoring, able to analyze transaction patterns and flag unusual behavior in real time.
- AI introduces new threats, including data poisoning, model manipulation, adversarial examples, and risks from centralized cloud infrastructure.
- Smart contracts stay transparent, while AI often acts as a “black box,” making audits and accountability harder.
- Security in AI-driven DeFi requires more than code audits — it calls for transparency, cryptographic proofs (like ZK), and on-chain tracking of AI decisions.
- ChangeNOW applies multilayered security, continuous monitoring, and expert collaboration to stay ahead of emerging threats.
- The rise of AI in crypto is more than hype, it enables smarter automation but must be paired with ethical practices, transparency, and strong user protections.
The Promise of AI in Crypto and DeFi
AI is not intrinsically unsafe; indeed, it can significantly enhance security and efficiency in crypto systems. In DeFi, machine learning and AI agents are being explored for tasks like automated trading, price prediction, and liquidity management. They can process vast on-chain data to optimize yields or balance pools, reacting to market moves faster than humans. AI also brings advanced fraud and risk detection tools to crypto. For example, ML-based systems monitor transaction patterns and can detect unusual activity. As highlighted in the article “How to Use AI to Automate DeFi Protocol Risk Audits” by Debangshu Chanda, these systems can “flag unusual behavior such as large withdrawals or sudden liquidity drains that may indicate an exploit,” **helping teams identify potential threats more efficiently.
In short, AI can become a guardian for DeFi by continuously analyzing codebases and transaction flows. According to an analysis by blockchain technology researchers, AI-powered DeFi audits use data from blockchain transactions, smart contract code, and external sources to detect anomalies and potential vulnerabilities in real time.
Beyond security, AI can automate compliance. Conversational bots can assist users on exchanges, and smart algorithms can help enforce KYC/AML rules. For example, AI-driven analytics are able to predict market swings and continuously monitor for money laundering risks in real time, as discussed in the in the paragraph Risk Management and Compliance: Protecting Users and Platforms. In practice, ChangeNOW already uses automated transaction monitoring and blockchain analytics to detect illicit fund flows; the introduction of AI would only sharpen this capability.
By leveraging AI, exchanges and DeFi platforms can provide faster, more accurate risk assessments, catch fraud as it happens, and even reduce the need for manually reviewing every transaction. This not only helps protect user funds but also builds regulatory confidence: institutional investors expect crypto services to “follow the latest legal requirements,” including robust security checks
In summary, AI’s potential in crypto is substantial: higher throughput, smarter services, and stronger automated defenses. But the very traits that make AI powerful, learning from data, acting autonomously, and often operating as a “black box”, also require new guardrails.
New Threat Vectors in DeFAI
Integrating AI into DeFi opens new attack surfaces. Unlike traditional smart contracts (which are open-source code and deterministic), AI agents are typically probabilistic and may run on off-chain infrastructure. This introduces several key risks; let's check it out.
To begin with, malicious actors could poison an AI’s training data or manipulate its model parameters. If an AI bot relies on price feeds or user data, an attacker might inject corrupted or misleading data so that the bot makes the wrong decisions (e.g., selling assets at the wrong time). In the Fintech Frontiers article “AI Data Privacy & Security Risks in Financial Systems ,” Narasimham Nittala discusses “data poisoning” and “model manipulation” as critical threats to AI-driven financial systems. For instance, when corrupted training data is used to manipulate fraud-detection algorithms. In the DeFi context, even a few adversarially crafted inputs could trick an AI agent into executing unprofitable trades or triggering faulty trades. According to Narasimham Nittala, AI-driven DeFi agents can potentially be exploited through model manipulation, data poisoning, or adversarial input attacks, enabling theft or system disruption
Another related threat is adversarial examples, subtly altered inputs that cause an AI to fail. For example, a tiny spoof in a price oracle or transaction data could push an AI-driven loan protocol into liquidating assets unfairly. In financial systems, adversarial inputs are a known concern; attackers might craft special transaction sequences or governance votes to mislead AI agents. These inputs can be nearly invisible to normal checks but can fool an AI’s logic. Jason Jiang, in his article “The Intersection of DeFi and AI Calls for Transparent Security” notes that adversarial inputs—subtly altered data—pose a new risk for DeFAI, as they can trick AI models into making incorrect decisions. Similarly, a financial risk report notes that adversarial manipulation of AI outputs is a real danger (an AI could hallucinate or leak sensitive data or be tricked into approving illicit transactions).
Also, many AI agents still run on centralized cloud servers (e.g., AWS, GCP). This reintroduces a single point of failure into an otherwise decentralized system. If an attacker compromises the server hosting an AI bot, they could override its actions entirely. Jason Jiang warns that relying on off-chain AI infrastructure “violates the decentralization ethos” and creates “a black box,” so transparency is diminished. We must guard against situations where one exploit (in cloud infrastructure or API keys) cascades through the whole protocol.
And let's not forget that AI systems can make opaque decisions based on complex logic. Unlike a simple smart contract, you cannot easily read all the decision paths of an ML model. This can hide vulnerabilities; a critical flaw might lurk in a rarely used branch of logic. Even well-intentioned AI (e.g., for yield optimization) could have a hidden strategy that inadvertently cheats users or violates policy. In summary, each extra layer of AI logic is also “one more line of code” that might be buggy or exploitable. Attackers in the AI+crypto world are creative. They could use AI themselves to find vulnerabilities faster (e.g., generative models simulating attacks). They could launch sophisticated multi-step hacks, for example, first attacking an AI’s data feed to alter an agent’s behavior, then exploiting the agent’s new bias. It’s clear that AI does not eliminate crypto risks; it can transform them. This is why the crypto community is calling for transparent security in DeFAI systems.
Transparent Auditing and Verification
Given these challenges, transparency and accountability are paramount. Just as every smart contract should be audited, every AI component must also be scrutinized, albeit with new techniques.
Key steps include:
Rigorous Third-Party Audits DeFi projects should commission professional security audits covering both smart contract code and any AI modules. Auditors must test not only the code logic but also the AI behaviors. This may mean auditing the AI’s training process, its data sources, and its retraining pipeline. Ideally, auditors simulate worst-case scenarios and “red-team” attacks: for example, deliberately feed malicious data to see if the AI misbehaves. As a DeFi-AI authority mentioned in Cointelegraph, these systems should be viewed as critical infrastructure, subjected to “skepticism, scrutiny, and worst-case scenario testing.” Thorough audits could catch hidden assumptions or insecure model updates before live deployment.
Open-Source and Documentation. Where possible, the code for AI models and their training scripts should be open-sourced or at least made inspectable. Transparency isn’t just code; it’s about trust. By publishing model architecture, version history, and update logs, a project demonstrates accountability. Open documentation of how decisions are made helps the community verify that the system is fair and robust. This mirrors how DeFi projects often publicize their governance and economic models for trust. It also aligns with security principles: audited projects “increase transparency and trust” and show investors they are serious about safety.
Cryptographic Verification. This cryptography can verify AI actions without exposing secret data. According to E. Glen Weyl and Michiel Bakker, in their research “Private, Verifiable, and Auditable AI Systems” ZKPs can prove that a model inference was executed accurately while keeping both the model’s parameters and the input data confidential. In practice, a DeFi-AI protocol might publish a ZK proof attesting that its AI bot executed a trade or decision according to its agreed-upon algorithm. This could allow on-chain verification: anyone could check that “yes, the AI used the official model and data.” Similarly, Merkle-tree attestation techniques could prove what data went into training or whether certain data was excluded. While these methods are still in the research stage, they point to a future where AI actions on blockchain are provably correct. ChangeNOW actively tracks such developments to see how we can adopt verifiable AI in our own systems.
Continuous On-Chain Attestation. Beyond one-time audits, projects should use on-chain “audit logs” for AI decisions. For instance, if an AI agent controls funds or rebalances a pool, each action can emit a signed transaction describing its trigger. External observers or decentralized oracles can check that the agent’s behavior matches its stated logic. This is similar to how DeFi often uses multi-sig or time locks as controls; an AI’s actions could be logged and audited retroactively. Combining on-chain records with the above cryptographic proofs could close the loop: we not only audit the model offline but also continuously verify it in operation.
Layered Testing and Monitoring. Code audit alone is not enough, right? Projects should deploy AI on testnets with live forks and simulate stress scenarios before mainnet use. After launch, real-time monitoring (possibly AI-assisted) should watch for unusual outputs or feedback loops. Ideally, multiple independent monitoring agents could cross-check each other. In short, security must be continuous, not a checkbox. As the Blockchain Council notesin How DeFi Audits Help Institutional Investors Avoid Risky Projects, security in DeFi is an ongoing process with new threats constantly emerging, making continuous checks essential. We apply the same mindset, emphasizing that pushing for ongoing security checks and regular updates is key to staying ahead of adversaries.
In practice, this means that new standards and best practices must evolve to govern the use of AI in crypto systems. The market is already recognizing this need. For example, E. Glen Weyl (Research Lead at Microsoft Research Special Projects and Senior Advisor for GETTING-Plurality at the Ash Center, Harvard) and Michiel Bakker (Professor at MIT Sloan and the MIT Institute for Data, Systems, and Society, and Senior Research Scientist at Google DeepMind) have proposed the establishment of an "AI Bill of Materials" — a transparency framework that outlines the components, models, and data sources involved in AI systems, similar to how software bills of materials are used in cybersecurity.
Regulatory bodies are also watching: the EU’s upcoming AI Act explicitly requires “high-risk AI systems” to be “resilient against attempts by unauthorized third parties to alter their use, outputs, or performance.” In other words, both the blockchain world and AI governance are converging on the same principle: transparency and tamper-resistance. ChangeNOW welcomes these developments, as they align with our core security philosophy.
Our stance is clear: innovation does not excuse poor security. We align with the view that AI-enhanced DeFi must be built “with security and transparency” from day one. So we will keep our community informed of any AI tools we deploy, publish security advisories, and openly share results. In doing so, we follow the maxim that transparency builds trust
Sources
[Jason Jiang, The Intersection of DeFi and AI Calls for Transparent Security, Cointelegraph] (https://cointelegraph.com/news/defi-ai-calls-for-transparent-security)
[Debangshu Chanda, How to Use AI to Automate DeFi Protocol Risk Audits, Idea Usher] (https://ideausher.com/blog/use-ai-to-automate-defi-protocol-risk-audits/)
[Blockchain App Factory, DeFAI: The Intersection of Blockchain and Artificial Intelligence in Finance] (https://www.blockchainappfactory.com/blog/defai-the-intersection-of-blockchain-and-artificial-intelligence-in-finance/)
[Narasimham Nittala, AI Data Privacy & Security Risks in Financial Systems, Fintech Frontiers] (https://fintechfrontiers.live/ai-data-privacy-security-risks-in-financial-systems/)
[E. Glen Weyl & Michiel Bakker, Private, Verifiable, and Auditable AI Systems, arXiv] (https://arxiv.org/html/2509.00085v1)
[Blockchain Council, How DeFi Audits Help Institutional Investors Avoid Risky Projects] (https://www.blockchain-council.org/cryptocurrency/how-defi-audits-help-institutional-investors-avoid-risky-projects/)
