Hardware Attack Graph Generation Using Large Language Models (LLMs)

As modern hardware becomes increasingly complex — spanning SoCs, FPGAs, IoT chips, secure enclaves, and firmware — the potential attack surface expands dramatically. Traditional approaches to hardware threat modeling often fall short in identifying multi-step, cross-layered exploit paths.

That’s where AI-powered attack graph generation comes in.

By leveraging Machine Learning (ML) and Generative AI, especially Large Language Models (LLMs), security researchers can now automate the construction of hardware attack graphs — dynamic visualizations of how a hardware system can be compromised through chained vulnerabilities.

Hardware Attack Graph Generation Using Large Language Models (LLMs)

🧠 What is a Hardware Attack Graph?

An attack graph is a directed graph where:

• Nodes = system states, components, or vulnerabilities

• Edges = transitions or actions an attacker can take to move closer to their goal (e.g., extract a key, gain debug access)

Unlike software attack graphs, hardware attack graphs must include:

• Microarchitectural vulnerabilities

• Firmware and bootloader paths

• Physical and side-channel attack vectors

• Trust boundary violations (e.g., DMA abuse, JTAG access)

These graphs help visualize entry points, privilege escalations, and attack chains, and they’re crucial in hardware threat modeling and secure design validation.

🤖 How AI Helps Generate Hardware Attack Graphs

1. LLM-Powered Component Understanding

LLMs (like GPT-4 or Claude) can:

• Parse data sheets, architecture manuals, and firmware

• Identify critical hardware blocks (e.g., memory-mapped registers, secure enclaves, IOMMU)

• Extract threat-relevant descriptions

Prompt example:

"Given the following SoC register map and bootloader flow, list possible hardware attack surfaces."

2. Vulnerability Linking and Path Chaining

Generative AI can reason across layers — linking:

• A firmware downgrade bug

• To an exposed UART bootloader

• To access to secure memory

This forms an attack path, e.g.:

UART access → Force bootloader mode → Bypass secure boot → Load malicious image → Dump TPM keys

LLMs are particularly good at identifying non-obvious linkages across hardware, firmware, and configuration states.

3. Graph Structure Generation

Using tools like:

• Neo4j for graph databases

• D3.js or Graphviz for rendering

• LLMs to output structured JSON/GraphML/CSV

You can prompt:

“Convert this list of vulnerabilities into a graph showing escalation from physical access to secure enclave compromise.”

AI outputs:

[
  { "source": "UART", "target": "Bootloader Control", "type": "access" },
  { "source": "Bootloader Control", "target": "Secure Boot Bypass", "type": "exploit" },
  { "source": "Secure Boot Bypass", "target": "Firmware Root Access", "type": "privilege escalation" }
]

🛠️ Components of an AI-Driven Attack Graph System

Component	Role
📄 Knowledge Extraction	Use LLMs to extract architectural details from manuals, firmware, or hardware specs
🧠 Vulnerability Mapping	Match known issues (CVE/NVD/CWE) to system components
🔗 Chaining Engine	Simulate attacker logic across steps
🕸️ Graph Builder	Generate structured visual graphs
📊 Risk Scorer	Assign severity or likelihood to each path using ML or heuristics

📍 Use Cases

• Secure Silicon Validation: Run AI to simulate potential attack paths during chip design reviews

• Firmware Penetration Testing: See how a bug in SPI flash can escalate to root access

• IoT Threat Modeling: Automatically identify privilege boundaries in smart home devices

• TPM / TEE Assessment: Map access paths from shared DMA to secure enclave leakage

🔬 Example: AI-Generated Path

Target: Smart Lock SoCInput to AI: Peripheral map, boot flow, and firmware configuration

Output:

• GPIO pin controls boot mode

• UART console exposes bootloader

• Bootloader lacks secure update check

• Attacker can downgrade firmware and execute unsigned image

• Leads to flash dumping and key extraction

🔗 Graph Path:[GPIO Tamper] → [UART Access] → [Bootloader Downgrade] → [Code Injection] → [EEPROM Key Dump]

⚠️ Challenges and Considerations

• Incomplete Documentation: LLMs work best when given structured info — gaps in vendor docs can lead to hallucinations.

• Realism vs. Theoretical Paths: AI might generate plausible paths that aren’t practically exploitable.

• Confidentiality: Use only public or authorized internal documentation when analyzing real hardware.

🔮 Future Directions

• RAG-based Graph Builders: Combine retrieval from CVE/NVD/SoC libraries with GPT-4-based graph generation.

• Auto-mitigator: LLMs that not only build the attack graph but also recommend countermeasures per node.

• Auto-formalization: Convert AI output into formal threat modeling standards like STRIDE, DREAD, or MITRE ATT&CK for Hardware.

✅ Conclusion

Generative AI is ushering in a new era of hardware threat modeling by automating the generation of attack graphs — turning complex system interactions into clear, visual paths from vulnerability to compromise. By bridging the gap between hardware documentation, firmware behavior, and attacker logic, AI-driven attack graph systems empower security teams to identify, prioritize, and mitigate risks early in the development lifecycle.