Reframing agents: not-human adversaries requiring new threat models

Dr. Ilia Shumailov - Former DeepMind AI Security Researcher, now building security tools for AI agents

Ever 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 economy

Oct 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=mlst

Submit 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 Shumailov

https://x.com/iliaishacked

https://iliaishacked.github.io/

https://sequrity.ai/

TRANSCRIPT:

https://app.rescript.info/public/share/dVGsk8dz9_V0J7xMlwguByBq1HXRD6i4uC5z5r7EVGM

TOC:

00:00:00 - Introduction & Trusted Third Parties via ML

00:03:45 - Background & Career Journey

00:06:42 - Safety vs Security Distinction

00:09:45 - Prompt Injection & Model Capability

00:13:00 - Agents as Worst-Case Adversaries

00:15:45 - Personal AI & CAML System Defense

00:19:30 - Agents vs Humans: Threat Modeling

00:22:30 - Calculator Analogy & Agent Behavior

00:25:00 - IMO Math Solutions & Agent Thinking

00:28:15 - Diffusion of Responsibility & Insider Threats

00:31:00 - Open Source Security Concerns

00:34:45 - Supply Chain Attacks & Trust Issues

00:39:45 - Architectural Backdoors

00:44:00 - Academic Incentives & Defense Work

00:48:30 - Semantic Censorship & Halting Problem

00:52:00 - Model Collapse: Theory & Criticism

00:59:30 - Career Advice & Ross Anderson Tribute

REFS:

Lessons from Defending Gemini Against Indirect Prompt Injections

https://arxiv.org/abs/2505.14534

Defeating 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/2503.18813

Agentic Misalignment: How LLMs could be insider threats

https://www.anthropic.com/research/agentic-misalignment

STOP ANTHROPOMORPHIZING INTERMEDIATE TOKENS AS REASONING/THINKING TRACES!

Subbarao Kambhampati et al

https://arxiv.org/pdf/2504.09762

Meiklejohn, 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=EH5PZW6aCr

Apache Log4j Vulnerability Guidance

https://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/2206.07840

Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches

David Glukhov, Ilia Shumailov, ...

https://proceedings.mlr.press/v235/glukhov24a.html

AlphaEvolve MLST interview [Matej Balog, Alexander Novikov]

https://www.youtube.com/watch?v=vC9nAosXrJw