Don't trust AI agents
This post argues that AI agents should always be treated as untrusted and potentially malicious, advocating for architectural solutions like containerization and strict isolation rather than relying on application-level checks. It contrasts a new project, NanoClaw, with existing approaches by emphasizing a minimal, auditable codebase and granular containment. Hacker News finds this topic highly relevant, grappling with the practical implications of securing intelligent, autonomous systems.
The Lowdown
The article posits that AI agents are inherently untrustworthy and must be designed with the assumption they will misbehave. Instead of relying on allowlists or permission checks, the author proposes an architectural approach to contain potential damage.
Key principles and features of NanoClaw's security model include:
- Process Isolation: Each AI agent runs within its own ephemeral container (Docker or Apple Container), created fresh per invocation and destroyed afterward. Agents run as unprivileged users with highly restricted filesystem access.
- Agent-to-Agent Isolation: Unlike systems where multiple agents share a single container, NanoClaw ensures each agent has its own separate container, filesystem, and session history, preventing information leakage between them.
- Defense-in-Depth: A mount allowlist further restricts what can be exposed, blocking sensitive paths by default and ensuring the host application code is mounted read-only.
- Auditable Codebase: NanoClaw consciously maintains a very small codebase (around 3,000 lines), making it fully auditable by a single developer, in stark contrast to projects with hundreds of thousands of lines of code.
- Modular "Skills" Architecture: New functionality is added via "skills" which are reviewed by the user, ensuring that only necessary and audited code integrates into the system, reducing the attack surface.
In conclusion, the article advocates for a security model built on "design for distrust," where containers, mount restrictions, and filesystem isolation contain the blast radius of misbehaving agents. It stresses that while AI agents inherently carry high risk, this risk can be managed by making trust as narrow and verifiable as possible.
The Gossip
Container Containment Concerns
Commenters debate the efficacy of Docker and similar container technologies as robust security boundaries for AI agents. While some acknowledge its role in isolation, skepticism arises regarding its ability to prevent sophisticated prompt injection attacks or determined breakouts, especially if the container still grants access to sensitive resources like email inboxes. The core concern is whether "containerization" is a sufficient answer to the complex and evolving threats posed by AI agents.
Code Bloat & Auditability
A significant point of discussion revolves around the challenge of auditing large codebases, especially those potentially generated or heavily influenced by AI. Commenters praise NanoClaw's approach of maintaining a small, human-reviewable codebase as a crucial security measure, contrasting it with projects that become too complex for any single individual or small team to fully comprehend and secure. This touches on broader implications for software development and trust in AI-assisted code generation.
The Obviousness of Distrust
Some users express that the central premise—"don't trust AI agents"—is an obvious truth. While acknowledging the need for architectural solutions, these comments imply that the core problem is widely recognized, and the discussion should focus more on innovative and truly robust solutions, rather than restating the initial distrust. It highlights a general consensus on the inherent risks of AI.
Practical Agent Permissions
One theme suggests that a simpler, more immediate solution to agent security is to severely limit their permissions from the outset. This approach champions giving agents only the bare minimum access required for their function, arguing that even sophisticated containment might be unnecessary if the agent fundamentally lacks the power to cause significant harm. This pragmatic view focuses on minimizing potential blast radius through permission restriction rather than complex sandboxing.