📰 Originally published on Securityelites — AI Red Team Education — the canonical, fully-updated version of this article.
⚠️ Authorised Research Only: GPT-4 and GPT-4o are OpenAI products tested under the OpenAI HackerOne bug bounty programme or in local research environments. Attack techniques documented here are for authorised security research only. Never apply these to production systems without explicit written authorisation.
GPT-4 is the most-tested AI model in the history of security research. Since its release in March 2023, thousands of researchers — from academic labs to individual bug hunters — have probed it systematically for vulnerabilities. What that research has produced isn’t a model that’s been made perfectly safe. It’s a detailed map of exactly where GPT-4’s attack surface is, how it differs across versions, and which attack techniques succeed at what rates.
I’ve been part of that research community. I’ve run authorised assessments using GPT-4 as the underlying model. I’ve replicated public findings and extended them. The picture that emerges is specific: GPT-4 has well-defined vulnerability patterns that differ meaningfully between model versions (GPT-4-turbo vs GPT-4o), between deployment contexts (direct API vs application wrapper), and between attack categories (injection vs jailbreaking vs extraction). Understanding these distinctions is what separates researchers who produce accurate, reproducible findings from those who fire random prompts and report whatever looks interesting.
🎯 What This Breakdown Covers
Five attack categories specific to GPT-4’s architecture and training
The attack surface differences between GPT-4-turbo and GPT-4o
Vision model attacks specific to GPT-4o’s multimodal capability
Function calling and tool use exploitation — the most underresearched GPT-4 surface
How GPT-4’s attack surface compares to Claude 3, Gemini 1.5, and Llama 3
⏱ 26 min read · 3 exercises included What You Need: Familiarity with the core attack categories from How to Hack AI Models · Understanding of the ChatGPT ecosystem from the vulnerabilities breakdown · Ollama for local testing exercises (Llama 3.1 as GPT-4-comparable target) ### GPT-4 Attack Techniques — Complete Breakdown 1. GPT-4’s Architecture and Why It Matters for Security 2. Attack Category 1 — Prompt Injection Specific to GPT-4 3. Attack Category 2 — System Prompt Behaviour Exploitation 4. Attack Category 3 — GPT-4-turbo vs GPT-4o Attack Surface 5. Attack Category 4 — Vision Model Attacks (GPT-4o) 6. Attack Category 5 — Function Calling Exploitation 7. GPT-4 vs Claude vs Gemini vs Llama — Attack Surface Comparison This tutorial deepens the model-specific layer of what we covered in how to test ChatGPT ethically. For the jailbreaking techniques that cut across all major models including GPT-4, the next article — AI jailbreak techniques that still work — covers the cross-model jailbreak landscape. The AI Elite Hub connects this GPT-4 specific content to the broader curriculum.
GPT-4’s Architecture and Why It Matters for Security Testing
GPT-4 is a large language model trained with Reinforcement Learning from Human Feedback (RLHF) to follow instructions and avoid producing harmful content. From a security research perspective, three architectural characteristics shape the attack surface in specific ways.
Safety training as a separate layer: GPT-4’s safety behaviour is primarily trained in rather than hard-coded. This means jailbreaking attacks are fundamentally attacks against trained behaviour rather than code-level constraints — which is why they’re probabilistic rather than deterministic, why they vary between model versions (each fine-tuning cycle changes the safety layer), and why novel framing attacks continue to find success even as specific known attacks are patched through further training.
Transformer context window: GPT-4 processes all content in its context window — system prompt, conversation history, and user input — through the same mechanism. There’s no hard architectural boundary between “trusted” and “untrusted” content from the model’s perspective. This is the fundamental condition that makes prompt injection possible: the model can’t reliably distinguish an instruction in the system prompt from a plausibly-formatted instruction embedded in user content.
Tool use via function calling: GPT-4’s ability to call external tools (web search, code execution, custom APIs) through the function calling interface creates an attack amplification layer. Getting the model to call a function with attacker-controlled parameters produces real-world effects — not just text outputs. This makes function calling exploitation one of the highest-severity attack categories against GPT-4-based applications.
Attack Category 1 — Prompt Injection Specific to GPT-4
GPT-4’s prompt injection attack surface has some specific characteristics that differ from other models. Understanding these characteristics helps target testing more efficiently.
Instruction sensitivity: GPT-4 is highly instruction-following compared to earlier models. This makes it more susceptible to cleanly formatted injection payloads that look like legitimate instructions. Payloads that use authoritative framing — “SYSTEM:”, “INSTRUCTION:”, markdown headers — have higher success rates against GPT-4 than against models that are less instruction-tuned.
Context position sensitivity: Injection payloads at the end of long contexts perform differently from payloads near the beginning. GPT-4’s attention mechanism distributes attention across the full context window, which means a payload buried deep in a long document retrieves less “weight” than the same payload at the end. I systematically test payloads at both positions on any RAG-based application.
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