AI Agents, Trust, and the Expanding Attack Surface
Autonomous AI agents are rapidly evolving from experimental curiosities into production-ready tools that can automate workflows, interact with users, and even execute transactions. But with that evolution comes a deeper question: how do we manage trust, identity, and security for systems that operate with autonomy both on and off-chain?
The Hidden Cost of Autonomy
Innovative platforms like OpenClaw have shown what happens when AI “skills” become a supply chain attack vector. Community-submitted skills, executable modules that extend agent behavior, have been found to contain malicious code or privileges that allow exfiltration, unauthorized actions, or persistence in user systems. SlowMist’s analysis flagged hundreds of risky modules in ClawHub registries, highlighting that the agent ecosystem is now an attacker target.
Similarly, the disclosed vulnerability in Moltbook AI’s infrastructure uncovered exposed credentials and API access, reminding us that even seemingly unrelated AI platforms have critical off-chain security risks when developers lean too heavily on auto-generated or loosely vetted code.
These incidents are symptomatic of a broader challenge in autonomous agent management.
Why On-Chain Trust Matters: ERC-8004’s Promise
One of the most interesting responses to this challenge is ERC-8004. As detailed in the EIP-8004 proposal, this is a new Ethereum standard designed to give autonomous agents a trust layer, an on-chain way to register identity, reputation, and discoverability that can be verified across tools and organizations.
AI agents today operate in a “trust vacuum”, without a standard way to prove who they are, what they’ve done, or who issued them authority. ERC-8004 aims to fill that gap with registries that can:
- Give agents persistent on-chain identities
- Enable reputation tracking across ecosystems
- Facilitate cross-organizational verification without centralized gatekeepers
This is an essential piece of infrastructure if AI agents are to operate in environments where money moves or contracts are signed and where trust cannot be assumed.
Security Tooling and the “Skills” Ecosystem
Nonetheless, the relationship between AI and security is not as straightforward. As agents proliferate, developers are building more powerful tooling, including security-focused “skills” that help audit, analyze, and secure code and agent behavior.
Trail of Bits Claude Code Skills, for example, offers a marketplace of Claude Code skills specifically for security research and vulnerability detection, including plugins for:
- Differential audit reviews
- Static analysis (Semgrep & CodeQL)
- Context-aware vulnerability analysis
These skills, while powerful, also illustrate a dilemma: security tooling lives in the same agent ecosystem we’re trying to secure. Without rigorous vetting and isolation, even code designed to defend can become part of the attack surface if misconfigured or compromised.
Schema-Constrained Autonomy for Agentic Finance
The reality is that on-chain trust standards like ERC-8004 are only part of the story. Leveraging the power of Agentic AI, but in contrast with open-ended agent execution is Orion Finance’s vault infrastructure, where AI-driven decision-making is deliberately constrained by a permissioned, schema-first design.
Orion assumes that Agentic AI, like Financial Machine Learning, is best applied as an allocation engine, not an execution authority. Strategists, human or AI, interact with vaults through a predetermined schema that expresses intent rather than arbitrary logic. Assets are whitelisted, execution paths are fixed, and autonomy is limited to proposing portfolio allocations.
This design aligns closely with quant-style financial machine learning pipelines: models optimize allocations and rebalance dynamically, but always within hard boundaries enforced on-chain. Orion supports both:
The key insight is that AI autonomy is preserved at the decision layer while being tightly guardrailed at the execution layer. No arbitrary code runs, no unexpected privileges are granted, and the attack surface is dramatically reduced, illustrating how off-chain intelligence and on-chain enforcement can coexist safely.
Engineering Trust for Autonomous Agents
We are witnessing the convergence of several key trends:
- AI agents becoming production tools that can execute meaningful action on behalf of users.
- ERC-8004 and similar standards aiming to bring trust and accountability into agent identity and reputation.
- Security tooling and marketplaces emerging alongside the agents they’re designed to support.
Taken together, these developments paint an exciting but cautionary picture: AI agents can revolutionize how we operate but only if we engineer trust deliberately.
Orion Finance Research
References
Koi. ClawHavoc: 341 Malicious ClawEdBot Skills Found by the Bot They Were Targeting.
CyberSecurityNews. Moltbook AI Vulnerability Exposed Credentials & API Keys.
Ethereum Improvement Proposals. EIP-8004: ERC-8004 Specification.
MEXC. ERC-8004 Overview.
Bankless. AI on Ethereum: ERC-8004.
Ethereum. Ethereum Is for AI While AI Agents Are for Ethereum.
Trail of Bits. Trail of Bits Skills Repository.