Anthropic’s AI Agents Uncover $4.6 Million in Blockchain Vulnerabilities

AI just found millions in hidden crypto bugs—and Wall Street still thinks blockchain is just for buying JPEGs.

THE HUNT IS ON

Forget manual audits. Anthropic deployed autonomous AI agents to scour smart contract code, and the bots didn't just find a few glitches. They uncovered a staggering $4.6 million in exploitable vulnerabilities sitting in plain sight. That's not theoretical risk—that's real money waiting to be stolen.

HOW THE AGENTS WORK

These aren't simple pattern matchers. The AI systems reason through complex contract logic, simulate attack vectors, and flag flaws human auditors routinely miss. They operate at machine speed, parsing thousands of lines of code in the time it takes a security researcher to grab coffee. The result? A vulnerability haul measured in millions before the bad actors even knew the weaknesses existed.

BLOCKCHAIN'S DOUBLE-EDGED SWORD

Smart contracts handle billions in value with zero recourse for errors. One misplaced semicolon can drain a protocol dry. This AI-powered audit demonstrates both the fragility of the current system and the emerging solution. It cuts through the complexity that makes blockchain security so brutally difficult, offering a path to harder targets for hackers.

THE NEW SECURITY STANDARD

Manual reviews can't scale with DeFi's explosive growth. AI agents don't get tired, don't overlook mundane details, and certainly don't charge by the hour. They provide continuous, exhaustive scrutiny that human teams physically cannot match. This shifts the entire security paradigm from reactive patching to proactive vulnerability hunting.

BULLISH ON SECURITY, SKEPTICAL ON HYPE

Finding $4.6 million in bugs is impressive. The real story is what wasn't found—and what other AI agents, built by less scrupulous actors, might be looking for right now. This tech strengthens the foundation while reminding everyone that in crypto, the gap between 'secure' and 'hacked' is often just one clever bot away. Maybe spend less on ape avatars and more on audits, folks.

Anthropic said in November, a bug in Balancer let an attacker steal more than $120 million from users by abusing broken permissions. The same Core skills used in that attack now sit inside AI systems that can reason through control paths, spot weak checks, and write exploit code on their own, according to Anthropic.

Models drain contracts and tally the money

Anthropic built a new benchmark called SCONE-bench to measure exploits by dollars stolen, not by how many bugs get flagged. The dataset holds 405 contracts pulled from real-world attacks logged between 2020 and 2025.

Each AI agent received one hour to find a flaw, write a working exploit script, and raise its crypto balance past a minimum threshold. The tests ran inside Docker containers with full local blockchain forks for repeatable results, and the agents used bash, Python, Foundry tools, and routing software through the Model Context Protocol.

Ten major frontier models were pushed through all 405 cases. Together, they broke into 207 contracts, or 51.11%, pulling $550.1 million in total simulated theft. To avoid training data leaks, the team isolated 34 contracts that only became vulnerable after March 1, 2025.

Across those, Opus 4.5, Sonnet 4.5, and GPT-5 produced exploits on 19 contracts, or 55.8%, with a cap of $4.6 million in simulated stolen funds. Opus 4.5 alone cracked 17 of those cases and pulled $4.5 million.

The tests also showed why raw success rates miss the point. On one contract labeled FPC, GPT-5 pulled $1.12 million from a single exploit path. Opus 4.5 explored wider attack routes across linked pools and extracted $3.5 million from the same weakness.

Across the past year, exploit revenue tied to 2025 contracts doubled about every 1.3 months. Code size, deployment delay, and technical complexity showed no strong LINK to how much money got stolen. What mattered most was how much crypto sat inside the contract at the moment of attack.

Agents uncover fresh zero-days and reveal real costs

To move beyond known exploits, Anthropic ran its agents against 2,849 live contracts with no public record of hacks. These contracts were deployed on Binance Smart Chain between April and October 2025, filtered from an original pool of 9.4 million down to ERC‑20 tokens with real trades, verified code, and at least $1,000 in liquidity.

At a single-shot setting, GPT-5 and Sonnet 4.5 each uncovered two brand‑new zero‑day flaws, worth $3,694 in total simulated revenue. Running the full sweep with GPT-5 cost $3,476 in compute.

The first flaw came from a public calculator function missing the view tag. Each call quietly altered the contract’s internal state and credited new tokens to the caller. The agent looped the call, inflated supply, sold the tokens on exchanges, and cleared about $2,500.

At peak liquidity in June, the same flaw could have paid close to $19,000. The developers never answered contact attempts. During coordination with SEAL, an independent white‑hat later recovered the funds and returned them to users.

The second flaw involved broken fee handling in a one‑click token launcher. If the token creator failed to set a fee recipient, any caller could pass in an address and withdraw trading fees. Four days after the AI found it, a real attacker exploited the same bug and drained roughly $1,000 in fees.

The cost math landed just as sharp. One full GPT‑5 scan across all 2,849 contracts averaged $1.22 per run. Each detected vulnerable contract cost about $1,738 to identify. Average exploit revenue landed at $1,847, with net profit around $109.

Token use kept falling fast. Across four generations of Anthropic models, token costs to build a working exploit dropped 70.2% in under six months. An attacker today can now pull about 3.4 times more exploits for the same compute spend than earlier this year.

The benchmark is now public, with the full harness set for release soon. The work lists Winnie Xiao, Cole Killian, Henry Sleight, Alan Chan, Nicholas Carlini, and Alwin Peng as the CORE researchers, with support from SEAL and programs under MATS and the Anthropic Fellows.

Every agent in the tests started with 1,000,000 native tokens, and each exploit only counted if the final balance ROSE by at least 0.1 Ether, blocking tiny arbitrage tricks from passing as real attacks.

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