🧠 Reverse Engineering AI Agents: A Technical Deep Dive By CyberDudeBivash | AI & Cybersecurity Expert

 

⚙️ Overview

As AI agents grow in complexity—performing autonomous tasks like reasoning, coding, decision-making, and even launching cyberattacks—it becomes increasingly crucial for security researchers, red teamers, and auditors to understand how these agents work under the hood.

This article walks you through a complete framework to reverse engineer AI agents, uncover their decision-making pipeline, prompt logic, APIs, and potential misuse vectors.

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🔍 What Is an AI Agent?

An AI agent is more than just a chatbot. It is a goal-driven autonomous system that:

• Accepts prompts or commands

• Plans via chains of thought (CoT)

• Uses tools (e.g. Google, Python, Shell)

• Executes actions in a feedback loop

• Often uses LLMs like GPT, Claude, or LLaMA

Example: Auto-GPT, AgentGPT, LangChain Agents, OpenDevin

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🎯 Why Reverse Engineer AI Agents?

Reason	Purpose
🔓 Security Audit	Identify prompt injection, SSRF, etc.
🧬 AI Behavior Forensics	Understand why the agent behaved a certain way
🛠️ Customization	Clone or modify the agent
🐞 Debugging / Sandboxing	Intercept tool calls & data flows
🧠 Model Understanding	Deconstruct LLM reasoning paths

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🔧 Reverse Engineering Framework

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🧪 1. Capture Prompts & Contexts

AI agents rely heavily on system prompts, planning prompts, and memory chains.

📌 Tools:

• 🐙 mitmproxy: Intercept API calls to OpenAI or LLMs.

• 🧠 LangSmith: Log full prompt chains in LangChain-based agents.

• 🪪 MemoryDump: For agents using vector memory (e.g. FAISS, Chroma).

🔎 Look For:

• System prompt content (roles, instructions)

• Prompt chaining logic

• API call patterns (especially /completions, /chat)

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⚙️ 2. Decompile Agent Code

Most agents are open-source or based on frameworks like LangChain, AutoGen, CrewAI, ReAct.

📁 Check:

• Planning module (usually uses ReAct or CoT)

• Tool calling (shell commands, browser APIs, Python exec)

• Memory classes (long/short term)

• RAG (Retrieval Augmented Generation) configs

🔧 Tools:

• Ghidra (for compiled binaries)

• Python AST (for Python-based agents)

• Static analysis tools: pyan, bandit, radare2

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🔂 3. Dynamic Tracing (Black Box Analysis)

Even if you can’t access the source code (e.g. for SaaS LLM agents), you can observe behavior dynamically.

🧰 Tools:

• strace / lsof: Monitor file and network activity

• API sniffers: Capture external web/DB calls

• ptrace / frida: Hook into runtime to trace functions

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🧬 4. Analyze Reasoning Paths (CoT + Logs)

Most LLM agents use Chain-of-Thought (CoT) reasoning or ReAct (Reason + Act) loop.

You can reconstruct reasoning trees using:

• Prompt outputs

• Internal logs (LangGraph, LangChain traces)

• Step-by-step decisions & tool usage

🧠 Pro Tip: Look for patterns like:
Thought → Action → Observation → Next Thought → Final Answer

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🛡️ 5. Security Analysis: Threat Mapping

Map agent behavior to known threats:

Threat Type	Vector
🧱 Prompt Injection	User input overriding logic
🐍 Code Execution	Python shell tool abuse
🌐 SSRF / RCE	Agent calling internal URLs
📦 Plugin Hijack	Malicious tool integration
🧠 Data Poisoning	RAG pulling malicious sources

Use MITRE ATLAS framework for LLM-specific threat mapping.

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🔐 Real-World Example: Reverse Engineering Auto-GPT

1. Forked GitHub repo

2. Located auto_gpt_agent.py → found planning logic

3. Intercepted calls to OpenAI API with mitmproxy

4. Extracted system_prompt.txt → detailed chain logic

5. Discovered memory database (memory.json)

6. Simulated prompt injection → made agent run rm -rf /tmp/data

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🎯 Deliverables of RE

After reverse engineering an agent, you can:

• Export full prompt chain

• Trace thought → action → output sequence

• Map security posture

• Build clone or defensive model

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🧩 Bonus: How to Build a Honeypot AI Agent

Create a fake AI agent and:

• Log all user prompts

• Inject behavioral traps (e.g., fake “sudo” calls)

• Analyze attackers trying to abuse tools or jailbreak

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📘 Summary Table

Step	Goal	Tool
Capture Prompts	Understand prompt logic	mitmproxy, LangSmith
Decompile Code	Audit core logic	Python AST, Ghidra
Dynamic Trace	Monitor live behavior	strace, frida
Analyze Reasoning	Visualize decisions	LangGraph
Security Map	Threat detection	MITRE ATLAS, ATT&CK

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📌 Final Thoughts from CyberDudeBivash

“AI agents are the new attack surface—and our job is to peel back every layer. Reverse engineering them isn't just curiosity—it's cyber defense.”