Risky Business Features

Everything is getting much worse, much faster

23 min · 5. juni 2026
episode Everything is getting much worse, much faster cover

Beskrivelse

In this podcast Brad Arkin joins James Wilson to talk about how the fear of being left behind in the AI era means enterprises are taking risks that would have been considered insane just a couple of years ago. Fears around outages or being hacked have been trumped by fears of being labelled an AI laggard. So where are we all going? Say hello to tech debt-riddled, vibe-coded apps, crazy dependencies on AI providers, and an emerging threat landscape that can’t be mitigated by a contemporary SOC. Sounds like fun, eh? SHOW NOTES

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30 episoder

episode Mythos on your desk? Using local LLMs for code reviews cover

Mythos on your desk? Using local LLMs for code reviews

In this podcast episode James Wilson chats with Karsten Nohl about his research into using local LLMs to replace cloud AI in security code reviews. In essence, Karsten created a hybrid code reviewing system where both cloud and local models are used to orchestrate, triage outputs, and write reports. In this system, only the local LLMs have source code access, with the cloud models used to manage the local models. In this “source-local” review technique, the source code never leaves the local endpoint, which is a requirement for some reviews. But funnily enough, Karsten was able to use this system to generate findings that were as impressive as when using frontier models directly. In a nutshell, Karsten proved it’s possible to use locally-hosted, open-weight models running on commodity hardware to produce findings comparable to those discovered by frontier cloud models. This episode is also available on YouTube [https://youtu.be/nhS5DTW0yzs]. SHOW NOTES * Beyond Fable: Can a Local LLM Replace Cloud AI for Security Code Reviews [https://srlabs.de/blog/beyond-fable] * Mythos smythos! How to find 0day with lesser models [https://risky.biz/RBFEATURES19/]

30. juni 20261 h 11 min
episode How using open weight models can blow up in your face cover

How using open weight models can blow up in your face

In this podcast episode James Wilson and Brad Arkin talk about how to safely use open weight large language models in the enterprise. The cost of frontier models was already driving interest in freely available open weight models like DeepSeek, Kimi and Qwen. But now the US government is forcing Anthropic to pull its Fable and Mythors models from the market, the argument for having greater control over your own AI stack is stronger than ever. But as you’ll hear in this episode, the model itself is just one component of the complex tech stack you’ll need to spin up if you want local inference. There’s a lot of moving parts, each of which comes with its own supply chain risks. So whether you’re hosting these models on your own hardware or via a SaaS provider, there’s a lot to ponder! SHOW NOTES

19. juni 202643 min
episode The state of the art in AI model jailbreaks cover

The state of the art in AI model jailbreaks

In this solo podcast episode, James Wilson breaks down the current state of AI model jailbreaks. If you’ve somehow missed the story, last week Anthropic released its Fable 5 and Mythos 5 models to the public. In the name of safety, both models were guardrailed up the wazoo, but that didn’t stop a bunch of jailbreakers from figuring out how to bypass at least some of their safety restrictions. In response to these guardrail bypasses the White House issued an export control directive on the models, citing national security concerns. But was the Trump administration right to do this? Do these jailbreaks represent a threat to the security of the USA, or was the export restriction overkill? Tune in to find out! SHOW NOTES * Pliny the Elder on Fable 5 Jailbreak [https://x.com/elder_plinius/status/2064776322979676227] * whoJumper's response to Pliny [https://x.com/whojumpr/status/2065413811184496894] * ConfusedPilot: Confused Deputy Risks in RAG-based LLMs [https://arxiv.org/abs/2408.04870]

16. juni 202652 min