AI Agents Hunt Smart-Contract Bugs: Faster Fixes, But Fueling Smarter Attackers

AI agents are hunting smart-contract bugs — and making attackers smarter at the same time

Executive summary

  • AI agents and continuous monitoring are transforming smart-contract security: always-on testing finds and patches vulnerabilities faster than human audits, but the same tools can be repurposed by attackers.
  • Reported losses reached roughly $3.4 billion in 2025, with the Bybit breach (~$1.4 billion) among the largest; open-source contracts now govern north of $100 billion in assets.
  • Tools like OpenAI’s EVMbench (tested with Paradigm and OtterSec) simulate real traffic and adversarial behavior; modern agents exploit >70% of identified flaws versus <20% for earlier models.
  • Leaders should adopt adversarial testing, layered governance, and clear human-in-the-loop policies to get the benefits of AI automation without enabling large-scale automated theft.

Why AI agents matter for smart-contract security

After several billion-dollar breaches, smart-contract security has shifted from occasional human audits to continuous, AI-driven monitoring. AI agents now scan live contracts, generate proof-of-concept exploits, propose patches, and retest fixes at a cadence humans can’t match. That speed matters because open-source contracts govern massive value and attackers can move faster than traditional controls.

Reportedly, attackers stole about $3.4 billion from blockchain platforms in 2025, with three incidents responsible for roughly 70% of that total. The largest single event cited was the Bybit exchange breach (~$1.4 billion). With more than $100 billion on chain under open-source governance, even small windows of vulnerability translate into enormous risk.

EVMbench and how adversarial testing works

OpenAI’s EVMbench — developed and trialed with partners like Paradigm and OtterSec — runs agents inside testnets (private or public networks that mimic mainnet behavior) to simulate real-world traffic and attacker tactics. The workflow looks like this:

  • Input: ABI, bytecode, transaction traces, and on-chain state.
  • Agent actions: fuzz inputs, craft complex call sequences, and generate exploit PoCs.
  • Remediation: propose patches or configuration changes; apply fixes in a sandbox.
  • Adversarial loop: re-run exploit attempts against patched code to verify the fix.

This adversarial loop hardens security quickly — but it also creates a dangerous double-edged sword.

“EVMbench lets agents simulate real-world pressure to see how contracts behave before they go live.”

Performance gains and the double-use problem

Reportedly, modern AI agents successfully exploit over 70% of identified vulnerabilities, compared with under 20% for earlier models. That jump compresses the detection-to-exploit timeline in favor of defenders, enabling faster patches and fewer funds at risk. However, the same automation, models, and playbooks can be weaponized by attackers who now have faster, cheaper ways to discover and scale exploits.

“Current AI agents are significantly better at exploiting vulnerabilities than their predecessors, raising concerns about misuse.”

That duality is the defining governance challenge: how to release and operate powerful testing tools without handing attackers a turnkey offensive capability.

From code security to autonomous finance: the next frontier

Technologists foresee AI agents moving beyond testing into financial operations. Jeremy Allaire has suggested AI agents will transact using stablecoins, and industry leaders like Changpeng Zhao have argued crypto could become a native payments layer for autonomous systems. That introduces a new risk vector: agent-initiated payments and programmable cashflows executed without direct human intervention.

“AI-operated wallets could help people avoid sending funds to wrong addresses or approving harmful transactions.”

VCs and security experts such as Haseeb Qureshi of Dragonfly advocate embedding intelligence into wallets to reduce accidental approvals and misdirected transfers. But automated wallets—if misconfigured or compromised—could also become a high-throughput attack surface.

Practical playbook for leaders: what to do now

AI for blockchain is an operational imperative and a governance challenge. The following actions are prioritized by timeframe so busy executives know where to focus first.

Immediate (next 30 days)

  • Enable continuous monitoring on critical contracts: deploy agent-based scanning on testnets or shadow environments immediately.
  • Establish incident metrics: start tracking MTTD (mean time to detect), MTTP (mean time to patch), and exploit success rate for your environment.
  • Inventory agent access: identify who has keys to powerful tooling and rotate credentials with limited-scope API keys.

Short-term (30–90 days)

  • Require adversarial testing before deployment: mandate red-agent runs (attacker-mode) and blue-agent verification (defender-mode) in CI/CD pipelines.
  • Implement human-in-the-loop gates for high-value actions: require explicit approvals for agent-initiated transactions above thresholds.
  • Pilot AI-assisted wallets for internal users: include address risk scoring, transaction labeling, and easy human override.

Strategic (90–180 days)

  • Design governance controls: RBAC, rate limits, segmented environments (separate red/blue agent environments), code-signing for patches, and immutable audit logs.
  • Update legal and insurance posture: work with counsel and insurers to reflect automated attack scenarios and faster incident lifecycles.
  • Adopt measurable SLAs for AI agents: target detection and remediation SLAs and require regular third-party audits of agent behavior and safety controls.

Governance, architecture, and technical controls

Concrete controls reduce dual-use risk while keeping automation effective:

  • Role-based access and limited-scope keys: grant minimal privileges for testing vs. live operations; segregate keys for simulation and production.
  • Rate limits and quotas: throttle agent activity to prevent mass scanning or automated exploitation outside sanctioned environments.
  • Segregated agent environments: run offensive (red) agents in isolated, audited sandboxes; keep training datasets air-gapped.
  • Code-signing and verification: require cryptographic signatures for patches and automated deployments; human approval for high-risk changes.
  • Immutable telemetry and audit trails: log agent inputs/outputs, decisions, and human overrides for ex post analysis and compliance.
  • Human override UX: design wallets and agent interfaces so humans can easily understand, pause, or rollback actions.

Metrics that show success (and risk)

Track these KPIs to prove the value of AI agents and to surface potential misuse:

  • MTTD (mean time to detect) for new vulnerabilities
  • MTTP (mean time to patch) after discovery
  • Exploit success rate in testnets vs. production
  • False positive rate for agent-generated alerts
  • Number of agent-initiated transactions and percentage requiring human approval
  • Time between agent detection and public disclosure or coordinated disclosure with stakeholders

What success looks like

A pragmatic target blends automation with human judgment: agents reduce MTTD by orders of magnitude, patches are validated in adversarial loops, and critical financial operations maintain a human approval barrier. Success also means governance frameworks prevent agent tooling from being a de facto exploit kit for attackers.

Key questions leaders should be able to answer

  • Why are AI agents being deployed for smart-contract security?
    AI agents detect vulnerabilities faster and provide continuous monitoring, shrinking the window attackers rely on.
  • Which tools and partnerships are leading this movement?
    Tools such as OpenAI’s EVMbench — developed with Paradigm and OtterSec — exemplify adversarial, AI-driven testing in testnets.
  • Aren’t these agents just helping attackers too?
    Yes — modern agents exploit a far higher share of flaws (>70%) than earlier models, so strict governance and environment controls are essential.
  • Will AI agents start transacting money autonomously?
    Industry voices expect AI agents to use stablecoins for programmable payments; this offers automation gains but adds systemic and security risks that must be managed.
  • What should we do first?
    Deploy continuous monitoring, require adversarial testing before releases, restrict access to powerful tools, and add human override mechanisms for high-value flows.

Final take

AI agents are rapidly becoming a core component of smart-contract security and financial automation. They reduce human error and compress response times, but they also accelerate attackers’ capabilities if governance lags. Treat AI agents as critical infrastructure: instrument them with KPIs, isolate offensive testing, embed human controls for large transactions, and update legal and insurance frameworks to match the new operational tempo. The choice is not whether to use AI automation — it’s how you use it safely.

Immediate steps (next 30 days)

  • Run an inventory of contracts with >$X exposure and enable agent scans for the top 10 risks.
  • Confirm RBAC and rotate API keys for all agent tooling; revoke unnecessary access.
  • Set baseline metrics for MTTD and MTTP and publish a response SLA for senior stakeholders.

Authors and contributors: security teams and product leaders should collaborate with legal, compliance, and external auditors when implementing these recommendations. For organizations managing digital assets, adopting these controls is mission-critical.

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