Publications
PoCo: Agentic Proof-of-Concept Exploit Generation for Smart ContractsPaper · Dataset
Evaluating Cryptographic API Misuse Detectors for GoPaper · Artifact
GoSurf: Identifying Software Supply Chain Attack Vectors in GoPaper · Tool
Geth Rebuild: Verifiable Builds for Go EthereumPaper · Tool
AI Agents Decline Free Beer🍺 but Have a Big Heart❤️Paper
UPPERCASE IS ALL YOU NEEDPaper
Talks
Dagstuhl Seminar 26192. Evaluating Logical Correctness in Agentic PoC Exploit Generation
Dagstuhl, Germany, 6 May 2026 · Seminar · Slides
A Dagstuhl talk focused on how we evaluated PoCo's logical correctness, including the methodology and criteria used to assess whether generated proof-of-concept exploits matched the intended vulnerability behavior.
SVM 2026. Evaluating Cryptographic API Misuse Detectors for Go
Rio de Janeiro, Brazil, 18 April 2026 · Slides
The first systematic study of cryptographic API misuse detection in Go, comparing four tools across 328 security-critical open-source projects. Presented at SVM 2026, co-located with ICSE.
Huawei Future Technology Device Summit. Agentic PoC Exploit Generation for Smart Contracts
Helsinki, Finland, March 2026 · Slides
Presenting PoCo's evaluation results on 23 real-world vulnerabilities to an industry audience, with discussion on scaling agentic exploit generation beyond smart contracts.
SEC-T 2025. Machine Learning for Offensive Cybersecurity
Munchenbryggeriet, Stockholm, September 2025 · Slides · Watch
How to weaponize AI and LLMs in real-world offensive security workflows, from adversarial use and agentic techniques to automated exploit generation for blockchain smart contracts (co-speaker: Sofia Bobadilla).
Teaching
EP120U Computer Systems, KTH Teaching Assistant. Responsible for teaching modules on operating systems, high-level languages, virtual machines, and assembly.
MSc Thesis Supervision, KTH
Supervisor.
Topics on machine learning for vulnerability detection, open source software analysis, and software supply chain security.
Finished supervised thesis:
Ouday Ahmed, An Empirical Study of Code Pre-trained Model Embeddings for Software Vulnerability Detection (Feb 2026).