Rock Lambros, Co-Lead of the 2026 OWASP Top 10 for LLM Applications, on why prompt injection would have fallen out of the top 10 based on incident data alone, and why the community kept it at number one anyway.

In this episode I sit down with Rock Lambros, longtime security leader, former CISO, core team member of the OWASP GenAI Security Project, and Co-Lead of the 2026 OWASP Top 10 for LLM Applications, to dig into the new list and the harder questions underneath it. We get into the letter from the project leads that opens the document, why the LLM and Agentic lists are converging, what a year of messy incident data actually told the working group, and why Rock argues that agency is not authorization.

In this episode:

• Why the LLM Top 10 and the Agentic Top 10 are merging in practice
• The opening letter, and why the industry was slow to admit the model was never the thing to secure
• Who benefits from a model-centric framing of AI security
• Prompt injection ranked number one by practitioners and out of the top 10 by the incident data
• Why incidents get reported by outcome rather than by initial vector
• Misinformation, the widest gap between the vote and the data
• Hidden Context Exposure, soft guardrails, and why instructions and data share one context window
• Context rot and why context window management matters for agents doing critical work
• Using AI to govern AI, and the dual-layer approach in the Agentic Control Standard
• Agency versus authorization, and why OAuth is what we have rather than the answer
• How to get involved in the OWASP GenAI Security Project

Chapters:
0:00 Intro and Rock's background
1:17 What the agentic work changed about the LLM Top 10
3:59 The opening letter and "It Was Never the Model"
8:48 Incident data and the prompt injection ranking
11:23 Misinformation and the limits of the data
14:24 Hidden Context Exposure and soft guardrails
17:18 The context window and context rot
19:57 Using AI to govern AI
22:49 Excessive agency, and agency versus authorization
27:09 How to get involved with OWASP

Connect with Rock:
LinkedIn: https://www.linkedin.com/in/rocklambros
OWASP GenAI Security Project: https://genai.owasp.org
Get involved: https://genai.owasp.org/contributing
OWASP Top 10 for LLM Applications 2026: https://genai.owasp.org/resource/owasp-genai-llm-top-10-2026/

Read my piece on the new list, It Was Never the Model: https://www.resilientcyber.io/p/it-was-never-the-model

Resilient Cyber: https://www.resilientcyber.io

Ondrej Vlcek, CEO of AISLE and former CEO of Avast, on why AI vulnerability discovery is not as commoditized as the industry thinks, and why remediation is still the real bottleneck.

In this episode I sit down with Ondrej Vlcek, Founder and CEO at AISLE. Ondrej spent roughly 30 years in cybersecurity, joining Avast as employee number six or seven doing kernel-mode driver work on Windows 95, eventually becoming CTO and then CEO, taking the company public and selling it to NortonLifeLock in a nearly $9 billion transaction. He co-founded AISLE in the fall of 2024 to close the loop from discovery through triage, remediation, and verification. His team has now disclosed 350 plus CVEs across projects like OpenSSL and curl.

We get into why the moat sits in the system and not the model, why the gray market price of vulnerabilities has not collapsed even as models get cheaper, and what it actually takes to ship a patch a maintainer will accept.

In this episode:
- Going from Avast intern to CEO, and why vulnerability management was the next problem
- The jagged frontier, and why bigger models do not always mean better results
- Which classes of bugs got cheap to find and which are still genuinely hard
- Why vulnerability prices have not collapsed despite all the model progress
- Building a model-agnostic system with bespoke benchmarks for model selection
- Sovereign AI, on-prem and air-gapped deployment, and why findings are the real crown jewels
- Triage, reachability, and why most findings are not actually exploitable
- Patch verification, regression risk, and mitigations for embedded systems that cannot be patched
- How AISLE earned trust from curl after Daniel Stenberg killed the bug bounty
- Whether a CVE count is a vanity metric
- Build versus buy as model capability keeps getting cheaper
- What breaks first in the CVE and open source maintainer ecosystem
- What AppSec leaders should change next quarter

Chapters
0:00 Intro
0:24 From Avast employee number six to a $9 billion exit
3:26 Why vulnerability management, and why now
5:15 The jagged frontier and what bigger models miss
10:36 The economics of finding bugs, and why prices have not collapsed
12:11 Building a model-agnostic system with real benchmarks
14:30 Sovereign AI, air-gapped deployment, and who sees your findings
19:42 Triage, reachability, and why remediation is the bottleneck
24:48 Patches that break things, and systems you cannot redeploy
25:39 curl, Daniel Stenberg, and death by a thousand slops
29:35 Is a CVE count a vanity metric?
31:34 Build versus buy when capability keeps getting cheaper
34:41 What breaks first in the next 18 months
39:46 What AppSec leaders should do next quarter
41:13 Closing

Ondrej Vlcek on LinkedIn
AISLE
AISLE research and blog

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Lovable CISO Igor Andriushchenko on soft guardrails vs. hard boundaries, securing vibe coding for non-developers, and building a security program at a 10x company.

I sit down with Igor Andriushchenko, Head of Security and CISO at Lovable, the AI development platform behind one of the fastest growth stories in the space. Igor joined as the first security hire when the company was around 40 people. A year later he is running a 20+ person team covering product security, GRC, IT, and platform safety for a company with 400 laptops in MDM and no sign of slowing down.

We get into what it actually takes to secure AI-native development, both inside a hypergrowth startup and on a platform where most of the people shipping software are not developers and definitely not security practitioners.

In this episode:

• Building a security program for the company you will be in 12 months instead of the one you are in today
• Soft guardrails versus hard guardrails, and how to decide which one a problem deserves
• Why hard blocks push AI-assisted workflows into the shadows
• Rooting guardrail decisions in business goals, risks, and threats rather than tool defaults
• Democratized development without democratized security, and what a platform owes the 99%
• Lovable's auto-fix toggle, per-app threat models, and the goal of an app with no security tab at all
• Whether models will ever produce secure code by default, and why defense in depth still carries the load
• Governing the reality that every employee vibe coding an app looks a lot like a new vendor
• GRC engineering as the way to measure control efficiency layer by layer against AI-powered attackers
• CRA, NIS2, and the EU AI Act landing on citizen developers who never thought of themselves as software manufacturers

Chapters: 0:00 Intro 0:23 Igor's background from DevOps to CISO 3:54 Scaling security at a 10x company 6:07 Reinventing the team when growth breaks it 08:26 Soft guardrails versus hard blocks 14:05 Tying guardrails to business risk 17:32 Democratized development, undemocratized security 18:52 Shared responsibility on an AI dev platform 21:16 Auto-fix, per-app threat models, and no security tab 25:21 Will models produce secure code by default? 29:56 Every employee vibe coding is a new vendor 30:57 Enterprise controls, publishing gates, and PII scanning 36:39 AI-powered attackers and why good enough changed 40:43 GRC engineering and measuring control efficiency 46:19 CRA, NIS2, and the citizen developer 52:41 Trust centers for builder apps 54:08 Closing thoughts on the vibe coding community

Guest links: Igor on LinkedIn: https://www.linkedin.com/in/igor-andriushchenko Lovable: https://lovable.dev

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