# uGen: An Agentic Framework for Generating Microarchitectural Attack PoCs

arXiv: https://arxiv.org/abs/2605.15503 · HTML: https://mlsanity.com/paper/2605.15503/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 32 of its 60 references that are in the map.
- First posted on arXiv 15 May 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 30 of 32)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [LLM Agents can Autonomously Exploit One-day Vulnerabilities](https://mlsanity.com/paper/2404.08144/) (2024)
- [InferFix: End-to-End Program Repair with LLMs](https://mlsanity.com/paper/2303.07263/) (2023)
- [Spectre Attacks: Exploiting Speculative Execution](https://mlsanity.com/paper/1801.01203/) (2018)
- [PentestAgent: Incorporating LLM Agents to Automated Penetration Testing](https://mlsanity.com/paper/2411.05185/) (2024)
- [HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing](https://mlsanity.com/paper/2412.01778/) (2024)
- [Jailbreaking and Mitigation of Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2410.15236/) (2024)
- [Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models](https://mlsanity.com/paper/2308.10462/) (2023)
- [D-CIPHER: Dynamic Collaborative Intelligent Multi-Agent System with Planner and Heterogeneous Executors for Offensive Security](https://mlsanity.com/paper/2502.10931/) (2025)
- [From CVE Entries to Verifiable Exploits: An Automated Multi-Agent Framework for Reproducing CVEs](https://mlsanity.com/paper/2509.01835/) (2025)
- [Unprecedented Code Change Automation: The Fusion of LLMs and Transformation by Example](https://mlsanity.com/paper/2402.07138/) (2024)
- [Towards Understanding Systems Trade-offs in Retrieval-Augmented Generation Model Inference](https://mlsanity.com/paper/2412.11854/) (2024)
- [CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution](https://mlsanity.com/paper/2505.17107/) (2025)
- [PoCGen: Generating Proof-of-Concept Exploits for Vulnerabilities in Npm Packages](https://mlsanity.com/paper/2506.04962/) (2025)
- [Flush+Flush: A Fast and Stealthy Cache Attack](https://mlsanity.com/paper/1511.04594/) (2015)
- [Debugging with Open-Source Large Language Models: An Evaluation](https://mlsanity.com/paper/2409.03031/) (2024)
- [AutoPentester: An LLM Agent-based Framework for Automated Pentesting](https://mlsanity.com/paper/2510.05605/) (2025)
- [A Systematic Study on Generating Web Vulnerability Proof-of-Concepts Using Large Language Models](https://mlsanity.com/paper/2510.10148/) (2025)
- [The Spy in the Sandbox -- Practical Cache Attacks in Javascript](https://mlsanity.com/paper/1502.07373/) (2015)
- [Spectre Returns! Speculation Attacks using the Return Stack Buffer](https://mlsanity.com/paper/1807.07940/) (2018)
- [SMoTherSpectre: exploiting speculative execution through port contention](https://mlsanity.com/paper/1903.01843/) (2019)

## Abstract (arXiv)

Microarchitectural attacks continue to evolve, uncovering new exploitation vectors in modern processors. From a defensive perspective, assessing a system's susceptibility to such attacks remains challenging. Developing functional attack implementations is labor-intensive, requires deep microarchitectural expertise, and is highly sensitive to execution environments. Consequently, existing attacks often lack portability, limiting systematic and scalable vulnerability assessment. Recent advances in large language models (LLMs) suggest a potential avenue for lowering these barriers. However, it remains unclear whether LLMs can reliably generate functionally correct microarchitectural attack code suitable for rigorous vulnerability testing. In this work, we present uGen, the first LLM-driven framework for automated microarchitectural attack code generation. A key challenge we address is identifying attack-specific knowledge gaps in LLMs. Through a systematic study of state-of-the-art models (GPT, Claude, and Qwen3), we find that LLMs frequently misgenerate or misplace critical attack primitives. Guided by this analysis, uGen employs a retrieval-augmented, multi-agent design that injects missing domain knowledge to synthesize functionally correct microarchitectural attack PoCs tailored to defender requirements. We evaluate uGen on cache-based and speculative-execution attacks across diverse set of microarchitectures, vulnerable functions, and LLM platforms. In the deployment stage, uGen achieves up to 100% success rate for Spectre-v1 (Claude Sonnet-4) and 80% for Prime+Probe (Qwen3-Coder). Finally, we demonstrate that uGen can generate a successful PoC code with a cost of 1.25 in under four minutes.