How to Secure Llama-Based AI Agents

SafeClaw by Authensor enforces deny-by-default safety policies on every tool call from Llama-based agents, whether you're running Llama models through Meta's API, Ollama, vLLM, or Together AI. Because Llama's tool calling output follows a structured format with function names and arguments, SafeClaw evaluates each invocation against your YAML policy before execution reaches your system.

How Llama Tool Calling Works

Llama 3 and 4 models support tool calling through a specific output format. When given tool definitions in the system prompt or via the API's tools parameter, Llama returns structured JSON with the tool name and parameters. The challenge with self-hosted Llama models is that you control the full stack — which means you're also responsible for the full security surface. There is no provider-side safety net between the model's output and your tool execution.

Llama Output → tool_call JSON → [SafeClaw Policy Check] → Execute or Deny

Quick Start

npx @authensor/safeclaw

Initializes a safeclaw.yaml in your project. SafeClaw parses Llama's tool call output format and applies your policies regardless of hosting provider.

Step 1: Define Policies for Llama Tool Use

# safeclaw.yaml
version: 1
default: deny

policies:
  - name: "llama-code-execution"
    description: "Control code execution from Llama agents"
    actions:
      - tool: "run_python"
        effect: allow
        constraints:
          timeout_ms: 10000
          blocked_imports: ["os", "subprocess", "shutil"]
      - tool: "run_shell"
        effect: deny

- name: "llama-file-access"
    description: "Restrict file operations"
    actions:
      - tool: "read_file"
        effect: allow
        constraints:
          path_pattern: "workspace/**"
      - tool: "write_file"
        effect: allow
        constraints:
          path_pattern: "workspace/output/**"
      - tool: "delete_file"
        effect: deny

- name: "llama-api-policy"
    description: "Control outbound API calls"
    actions:
      - tool: "http_request"
        effect: allow
        constraints:
          method: "GET"
          url_pattern: "https://api.internal.com/**"
      - tool: "http_request"
        effect: deny

Step 2: Integrate with Ollama

If you're running Llama locally via Ollama:

import { SafeClaw } from "@authensor/safeclaw";

const safeclaw = new SafeClaw("./safeclaw.yaml");

const response = await fetch("http://localhost:11434/api/chat", {
  method: "POST",
  body: JSON.stringify({
    model: "llama3.3",
    messages: [{ role: "user", content: "Analyze the CSV in workspace/data.csv" }],
    tools: [
      {
        type: "function",
        function: {
          name: "read_file",
          description: "Read a file from disk",
          parameters: { type: "object", properties: { path: { type: "string" } } },
        },
      },
    ],
  }),
});

const data = await response.json();

if (data.message?.tool_calls) {
  for (const toolCall of data.message.tool_calls) {
    const decision = safeclaw.evaluate(
      toolCall.function.name,
      toolCall.function.arguments
    );
    if (decision.allowed) {
      await executeTool(toolCall.function.name, toolCall.function.arguments);
    } else {
      console.log(Blocked: ${toolCall.function.name} — ${decision.reason});
    }
  }
}

Step 3: Integrate with vLLM or Together AI

For production Llama deployments using vLLM's OpenAI-compatible endpoint:

import OpenAI from "openai";
import { SafeClaw } from "@authensor/safeclaw";

const client = new OpenAI({
  baseURL: "http://your-vllm-server:8000/v1", // or Together AI endpoint
  apiKey: process.env.VLLM_API_KEY,
});
const safeclaw = new SafeClaw("./safeclaw.yaml");

const response = await client.chat.completions.create({
  model: "meta-llama/Llama-3.3-70B-Instruct",
  tools: [/ your tool definitions /],
  messages: [{ role: "user", content: "Process the pending orders" }],
});

const message = response.choices[0].message;
if (message.tool_calls) {
  for (const call of message.tool_calls) {
    const args = JSON.parse(call.function.arguments);
    const decision = safeclaw.evaluate(call.function.name, args);
    // Execute or deny based on decision.allowed
  }
}

Because vLLM and Together AI use the OpenAI-compatible format, SafeClaw integration is identical to OpenAI — your policies work across providers without modification.

Step 4: Handle Llama's Tool Call Parsing Edge Cases

Self-hosted Llama models can occasionally produce malformed tool call JSON. SafeClaw's parser handles common failure modes — partial JSON, missing fields, extra whitespace — and treats any unparseable tool call as a deny by default.

policies:
  - name: "malformed-call-handling"
    actions:
      - tool: "__parse_error__"
        effect: deny
        log_level: warn

Why SafeClaw

• 446 tests covering policy evaluation, edge cases, and audit integrity
• Deny-by-default — critical for self-hosted models where you own the full security stack
• Sub-millisecond evaluation — no added latency to your local inference pipeline
• Hash-chained audit log — tamper-evident logging for compliance and debugging
• Works with Claude AND OpenAI — and Llama via any OpenAI-compatible endpoint

Related Pages

• How to Secure Your OpenAI GPT Agent
• How to Add Safety Controls to Google Gemini Agents
• How to Add Safety Gating to LangChain Agents
• How to Secure CrewAI Multi-Agent Systems