Bankless Podcast Episode Summary

Title: AI Finds 70% of Smart Contract Exploits | Alpin Yukseloglu
Date: March 5, 2026
Host: Bankless
Guest: Alpin Yukseloglu, Investment & Research Partner, Paradigm; Co-author of EVM Bench
Theme: Exploring the intersection of AI and crypto security, focusing on how rapidly-advancing AI models are transforming smart contract auditing, exploit detection, and the wider DeFi ecosystem.

Episode Overview

This episode features an in-depth conversation with Alpin Yukseloglu about the accelerating capabilities of AI in smart contract security. With the release of EVM Bench—a benchmark developed with OpenAI—AI models can now detect and exploit over 70% of historical critical smart contract vulnerabilities, a leap from just 13% six months prior. The discussion ranges from existential risks of superintelligent AI, the evolving arms race between attackers and defenders in DeFi, the philosophical implications of advancing technology, and how the crypto ecosystem must adapt to this new AI-powered frontier.

Key Discussion Points & Insights

1. AI’s Impact on Crypto Security (00:32–04:01)

• Long-term vs. Short-term Threats:
  • Long-term, AI brings the promise of near-perfect security to crypto, greatly raising the ceiling for growth and institutional confidence.
  • Short-term, there’s an urgent need for the industry to proactively defend against fast-evolving AI-powered exploits.
• Blistering Pace of Advancement:
  • Six months ago, AI agents could only detect 12–13% of critical bugs; the latest models now exceed 70%, demonstrating exponential improvement.
  • Quote (Alpin, 01:54):
    “From less than 20% to over 70%... these things are just growing at a blistering pace.”

2. Existential Risks and AI Superintelligence (06:55–12:52)

• Security at the Frontier:
  • The conversation explores what happens when AI surpasses human capacity—including unexpected cryptographic breakthroughs.
  • Physical and mathematical constraints (e.g., laws of physics, chaos theory) may limit even AI’s power.
• Psychological Response to the Singularity:
  • Facing the “singularity” requires balancing agency with acceptance—avoid both denial and fatalistic acceptance.
  • Quote (Alpin, 11:16):
    “The core point is agency... we have agency over these outcomes and that you yourself can bend the arc of the future.”

3. Agency in the Age of AI (12:52–19:13)

• Call for Active Participation:
  • Fear and paralysis come from passivity; agency and groundedness come from working with the frontier.
  • Crypto’s culture and institutions (like Paradigm) are deeply grounded in this proactive approach.
  • Quote (Host, 16:23):
    “If that singularity is intimidating to you... grab a mop, do something. Like there’s work to be done.”

4. Smart Contract Vulnerability Landscape (19:13–22:50)

• Most at Risk:
  • Long-tail, small-cap protocols (especially on EVM chains) are most vulnerable as inference costs drop.
• OG Contracts:
  • Older, high-value contracts are safer for now but invite larger prizes for attackers and demand sustained vigilance.
  • Quote (Alpin, 21:01):
    “There will be smaller protocols that are less secure... fall first. We’ll have to look out for the first exploit that is almost entirely from AI.”

5. EVM Bench: How AI Models Are Evaluated (22:50–30:09)

• Benchmark Structure:
  • Detect, Patch, Exploit: Evaluates whether AI can:
    1. Find bugs
    2. Patch bugs
    3. Exploit bugs, with the last being the most novel and valuable contribution.
  • Verifiability:
    • EVM Bench uses production-grade EVM environments; models must produce proof-of-concept exploits, reducing false positives to near-zero.
  • Quote (Alpin, 26:19):
    “The core thing… is the benchmark: how good are the models at exploiting smart contracts?”
• Blazing Progress:
  • AI models went from 20% → 50% → 70% exploit detection rates in just months with minimal new data, as crypto’s verifiability turbo-charges training.

6. AI, Data, and Crypto’s Unique Suitability (29:48–39:32)

• Crypto Is Highly “Learnable”:
  • Verifiability means AI can train fast with less data—solidity and EVM ecosystems are improving quickly.
• Social/Perception Barriers:
  • AI labs have been slow to embrace crypto due to stigma, reputation risk, and negative experiences—but this is changing.
  • Quote (Alpin, 34:50):
    “My sense is that it's almost entirely a social thing... crypto is the biggest industry that has remained the most contrarian.”

7. AI-Crypto Co-evolution & Network Effects (39:32–49:37)

• Why Security First?
  • Security is economically valuable, intelligence-bound, and easily verifiable.
• Possible Future Capabilities:
  • Expect advances in mechanism design, MEV extraction, protocol-layer attacks, transaction crafting, etc.
• Surface for Attack/Defense:
  • Eventually, all contracts might be under continuous AI-powered audit—by whitehats and blackhats.
  • Quote (Host, 43:39):
    “The amount of assets that can be sustained on these networks is proportional to how secure they are.”

8. Formal Verification & the Road Ahead (54:23–57:19)

• AI as Formal Verification Aid:
  • AI could help scale formal verification across the crypto stack, though specs are still a human bottleneck.
  • Quote (Alpin, 55:02):
    “All the best software will probably end up being formally verified... and [AI] accelerates that.”

9. Alpin’s Personal Conviction in Crypto (57:19–61:03)

• Why Crypto?
  • Intellectual challenge, contrarian opportunity, network effects, and a global, extra-sovereign safety net.
  • Crypto is uniquely suited for an era of rapidly commoditized intelligence and instability.
  • Quote (Alpin, 57:43):
    “It’s been extremely intellectually interesting... it’s remained extremely contrarian in ways where I can put my finger on exactly what they’re missing.”
• Crypto and AI—Mutual Leverage:
  • If guided well, AI’s progress can massively strengthen crypto, and crypto offers unique benefits to AI.

Timestamps & Notable Quotes

• 00:44 | Alpin:
  “AI is going to be extremely, extremely good for crypto… but the models are getting extremely good, strikingly good, and it’s very important that we position the industry defensively.”
• 02:38 | Alpin:
  “Right now the models are quite good, but they’re not better than the best human auditors… but once we hit [superhuman AI], this will just completely break all of our assumptions.”
• 08:10 | Alpin:
  “If you try to do this at the limit… you end up leading to very odd places… the best we can do right now is to get ourselves into the frontier… and be ready when those inflections happen.”
• 21:01 | Alpin:
  “There will probably be this canary in the coal mine effect… we’ll have to look out for the first exploit that’s almost entirely from AI, and then the race will be on.”
• 26:19 | Alpin:
  “The core release… is the benchmark. How good are the models at exploiting smart contracts.”
• 29:48 | Alpin:
  “Verifiability was very important… if the agent tells you that it found a bug, it literally has a proof of concept that it can exploit against a production-grade EVM environment and drain money from a contract.”
• 34:50 | Alpin:
  “It’s almost entirely a social thing… crypto is the biggest industry that has remained the most contrarian… it hasn’t been in the Overton window of anyone in the valley.”
• 43:39 | Alpin:
  “The amount of assets that can be sustained on these networks is proportional to how secure they are.”
• 55:02 | Alpin:
  “With time, all the best models, all the best software will probably end up being formally verified.”
• 57:43 | Alpin:
  “It’s just... it’s remained extremely contrarian among my smartest friends in a way where I can put my finger on exactly what they’re missing… it’s just really exciting.”
• 60:01 | Alpin:
  “It’s not—obviously, nothing is guaranteed... but for fundamental reasons, crypto is extremely good for AI, and AI is extremely good for crypto.”

Conclusion

This episode underscores a pivotal moment for both crypto and AI: AI capabilities in code understanding and smart contract security are soaring, promising both unprecedented risk and, ultimately, a path to robust, self-sustaining security. The interplay between offensive and defensive use of AI is still uncertain, but proactive engagement, not “doomerism,” is urged. EVM Bench is a landmark for both industries—ushering in a new era where crypto is not only a novel financial substrate but possibly the world’s best testbed for AI progress.

Recommended Next Steps for Listeners:

• For smart contract developers and protocol teams: actively integrate AI-based defensive tools and participate in benchmarking efforts like EVM Bench.
• For AI practitioners: Engage with verifiable, economically relevant crypto data for model evaluation and training.
• For all: Cultivate a mindset of agency and adaptability—move fast, stay engaged, and help shape this converging future.