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Eliminating entire classes of vulnerabilities, at the scale of billions of users.

I'm Lukas Weichselbaum, a Senior Staff Security Engineer and Manager on Google's Product Security team. I lead two teams with one philosophy: secure by design. Web Security turns vulnerability classes like XSS into compile errors. AI Agent Security keeps agents from going rogue. Now that agents write much of the code, I work on making that code safe by construction too: high-assurance agentic engineering.

new It's never been easier to write (in)secure software the OWASP AppSec Days Portugal 2026 keynote, written out · ~35 min

Zürich, CH

// 01 · work @ googlesecure by design, not by chance

Web Security has been most of my career. We embed security primitives across the SDLC, into core frameworks, Chromium and more, so Google's 1,300+ web applications are secure-by-design, not secure-because-someone-remembered. Lately that includes Agentic Web Security: using agents to change how classical web security gets done.

AI Agent Security is the team I started in 2023, focused on securing the agents themselves: containing prompt injection before it turns into rogue actions or sensitive-data exfiltration. Our approach is public at saif.google. The two teams feed each other: building with agents teaches us how to secure them, and the agent security we build lets us ship more powerful security agents.

// 02 · selected worksix things worth your time

// 03 · ~/aboutone problem, several directions

Since 2015 I've worked on one problem from several directions: making web vulnerabilities impossible instead of merely findable.

I lead Google's Web Security team, responsible for more than 1,300 web applications, Gmail, Search and YouTube among them. I also lead its first AI Agent Security team, which I bootstrapped in 2023 to do for agents what we spent a decade doing for the web. Together that is about 25 engineers worldwide and one approach, secure by design: embed the security primitive in the framework, the browser or the model instead of asking hundreds of product teams to each get it right.

Before Google I was a security consultant at SEC Consult in Vienna, 60+ audits and forensic engagements, and studied at the Technical University of Vienna at the same time. There I co-founded Andrubis, one of the first large-scale dynamic-analysis platforms for Android malware; it analysed over a million apps.

Press: PortSwigger's Daily Swig (Fetch Metadata, Google I/O 2019), ZDNet, eWeek.

cat ~/cv → full chronological record

2026High-assurance agentic engineeringkeynote, OWASP AppSec Days Portugal · the benchmark behind it published (write-up)
2023 →AI Agent Securitybootstrapped Google's first AI Agent Security team · SAIF (approach)
2018Web Security leadbuilt and led the org securing Google's web apps (deck)
2016CSP Is Dead, Long Live CSP!ACM CCS · strict-dynamic · CSP Evaluator (deck)
2013→ Google ZürichVendor Security · open-sourced VSAQ
2012Andrubis · Technical University of ViennaAndroid malware analysis at scale · Google internship
2009SEC Consult, Viennasecurity consultant · 60+ audits

// 04 · ~/workby theme, not by org chart

web platform security

The thesis: XSS and other common web vulnerabilities survive because we ask every developer to be perfect. Strict CSP, Trusted Types, Fetch Metadata, and COOP move the burden into the platform. My team and I rolled these out across Google's most sensitive surfaces: Gmail, Search, YouTube, Drive, Docs, Cloud Console, Accounts. It took years, because an outage there is measured in millions. Today these protections cover the majority of Google's sensitive Front End traffic.

It compounds in the high-assurance frameworks. Across the hundreds of complex web applications built on them, we've "averaged less than one XSS report per year in total." XSS in core Google products is down 90% over the past decade (An Overview of Google's Commitment to Secure by Design, Google, 2024, p. 7). How we achieved this is public: Secure by Design (deck).

agentic web security

Not securing agents: using agents to change how classical web security gets done. I set up Google's three-part strategy: find agents that discover vulnerabilities autonomously (Pagebreak, written up on the Google Security Blog); patching agents that generate and ship fixes automatically, work that contributed to Google DeepMind's CodeMender; and teaching the underlying models safe coding, so AI-written code uses safe APIs by default. The same tools are changing how we clear hardening backlogs and run the VRP.

high-assurance agentic engineering

Agents now write a large share of the world's code, and they reproduce the vulnerable patterns they were trained on, at machine speed. The fix is the one we already had. Safe coding asks developers to accept hardened APIs, opinionated frameworks and enforcement they cannot opt out of, and developers push back. Agents don't: they loop against a verdict until it is green. Keep the security invariants outside that loop, refused at compile time, re-checked at commit time, enforced by the browser at runtime, and the outcome no longer depends on the model. The human does not leave the loop; the human moves up it, from approving diffs to writing the assurance.

We measured it. Nine models from two vendors built the same rich-text notes app 54 times on a bare scaffold with no headers and 54 times inside the harness, scored by the same judge. Vibe-coded, 9 of 53 working builds executed an XSS payload and 31 still wrote to innerHTML behind an escaper the model happened to write that day. Harnessed, 0 of 54, with no sink left to bypass. Not faster per build: 1.2 to 2× the turns, spent on verdicts instead of guesses. The keynote is written out here; the benchmark, every run's scorecard included, is on GitHub.

ai agent security

The other team I lead secures the agents themselves. Agents are non-deterministic and take in untrusted input; they can be manipulated into rogue actions or sensitive-data exfiltration, and there is no known way to prevent prompt injection outright. So we contain it: a hybrid defence-in-depth that pairs deterministic policy enforcement with model-based defences, limits an agent's powers, and keeps its actions observable. Our approach is a core part of Google's public Secure AI Framework, and the team's paper, Google's Approach for Secure AI Agents, became one of the most-cited papers in the field, second only to "Attention Is All You Need" on Google Research's popularity chart. In practice, the same team helps secure Gemini and Search's AI features.

measurability

You can't secure what you can't measure. I bootstrapped Google's Product Security Measurability programme and co-authored Security Signals (NDSS MADWeb 2025): security posture extracted from production HTTP traffic at the reverse proxy, across 8,000+ services and ~1,000 domains, trillions of requests, used by 60+ teams. The synthetic-signals approach behind it is patent-pending. On top of it: Long-Lived Stable Metrics for Web Security, Memory Safety and Crypto Hygiene, measuring how much of the ecosystem is secure by architecture, not how many bugs are open. It's how we track adoption of the hardened frameworks, and increasingly of our AI agents, turning "are we secure by design?" into a number leadership can act on.

tools

// 05 · ~/specsW3C WebAppSec WG · member since 2019

Beyond the specs, I led multi-year collaborations with Mozilla, Apple and Igalia to bring Trusted Types, Fetch Metadata, and CSP3 to Firefox and Safari. After six years, Trusted Types now ships in every major browser engine, and as of February 2026 all four specs above are Baseline. A spec only counts once every engine implements it.

// 06 · ~/talks31 talks · 6 guest lectures · since 2009

Featured decks, viewable in the browser →

university guest lectures

programme committees & peer review

NDSS MADWeb (2021) · USENIX WOOT (2020, 2019)

// 07 · ~/pubsresearch.google · scholar · dblp

articles & write-ups

// 08 · ~/contact

Let's make the web secure by design.

reach out: talks, collaboration, research