AI Agents Can Code 10,000 Lines of Hacking Tools In Seconds - Dr. Ilia Shumailov (ex-GDM)
Dr. Ilia Shumailov - Former DeepMind AI Security Researcher, now building security tools for AI agentsEver wondered what happens when AI agents start talking to each other—or worse, when they start breaking things? Ilia Shumailov spent years at DeepMind thinking about exactly these problems, and he's here to explain why securing AI is way harder than you think.**SPONSOR MESSAGES**—Check out notebooklm for your research project, it's really powerfulhttps://notebooklm.google.com/—Take the Prolific human data survey - https://www.prolific.com/humandatasurvey?utm_source=mlst and be the first to see the results and benchmark their practices against the wider community!—cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economyOct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst— We're racing toward a world where AI agents will handle our emails, manage our finances, and interact with sensitive data 24/7. But there is a problem. These agents are nothing like human employees. They never sleep, they can touch every endpoint in your system simultaneously, and they can generate sophisticated hacking tools in seconds. Traditional security measures designed for humans simply won't work.Dr. Ilia Shumailovhttps://x.com/iliaishackedhttps://iliaishacked.github.io/https://sequrity.ai/TRANSCRIPT:https://app.rescript.info/public/share/dVGsk8dz9_V0J7xMlwguByBq1HXRD6i4uC5z5r7EVGMTOC:00:00:00 - Introduction & Trusted Third Parties via ML00:03:45 - Background & Career Journey00:06:42 - Safety vs Security Distinction00:09:45 - Prompt Injection & Model Capability00:13:00 - Agents as Worst-Case Adversaries00:15:45 - Personal AI & CAML System Defense00:19:30 - Agents vs Humans: Threat Modeling00:22:30 - Calculator Analogy & Agent Behavior00:25:00 - IMO Math Solutions & Agent Thinking00:28:15 - Diffusion of Responsibility & Insider Threats00:31:00 - Open Source Security Concerns00:34:45 - Supply Chain Attacks & Trust Issues00:39:45 - Architectural Backdoors00:44:00 - Academic Incentives & Defense Work00:48:30 - Semantic Censorship & Halting Problem00:52:00 - Model Collapse: Theory & Criticism00:59:30 - Career Advice & Ross Anderson TributeREFS:Lessons from Defending Gemini Against Indirect Prompt Injectionshttps://arxiv.org/abs/2505.14534Defeating Prompt Injections by Design. Debenedetti, E., Shumailov, I., Fan, T., Hayes, J., Carlini, N., Fabian, D., Kern, C., Shi, C., Terzis, A., & Tramèr, F. https://arxiv.org/pdf/2503.18813Agentic Misalignment: How LLMs could be insider threatshttps://www.anthropic.com/research/agentic-misalignmentSTOP ANTHROPOMORPHIZING INTERMEDIATE TOKENS AS REASONING/THINKING TRACES!Subbarao Kambhampati et alhttps://arxiv.org/pdf/2504.09762Meiklejohn, S., Blauzvern, H., Maruseac, M., Schrock, S., Simon, L., & Shumailov, I. (2025). Machine learning models have a supply chain problem. https://arxiv.org/abs/2505.22778 Gao, Y., Shumailov, I., & Fawaz, K. (2025). Supply-chain attacks in machine learning frameworks. https://openreview.net/pdf?id=EH5PZW6aCrApache Log4j Vulnerability Guidancehttps://www.cisa.gov/news-events/news/apache-log4j-vulnerability-guidance Bober-Irizar, M., Shumailov, I., Zhao, Y., Mullins, R., & Papernot, N. (2022). Architectural backdoors in neural networks. https://arxiv.org/pdf/2206.07840Position: Fundamental Limitations of LLM Censorship Necessitate New ApproachesDavid Glukhov, Ilia Shumailov, ...https://proceedings.mlr.press/v235/glukhov24a.html AlphaEvolve MLST interview [Matej Balog, Alexander Novikov]https://www.youtube.com/watch?v=vC9nAosXrJw
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