Paul

@pauls-research.bsky.social

I <3 thinking. Context is key! Data, AI, Graph and Cybersecurity. Used to hunt exploit kits, (was) demon117 on twitter

Initial thoughts of getting Pi running locally (GLM 5.2) is this thing has straight up conversations with itself and refines on the fly. Thankfully I've already paid for all the tokens it'll use.

Pi Hermes or building something with Lang Chain? Need sandboxing and ways to access it via CLI, but not running on the sandbox over SSH. I got the brain running, just need the harness...

What are the key points of interest? 1. Detection and Response for an advanced attack 2. Frontier models' guardrails failing defenders 3. Long Horizon capabilities I'm in short horizon mode, listening to people w/ long horizon experience as if we're in a cave describing shadows

Great readout by @gadi after a CISO session on the huggingface/openai debacle. Real, helpful, practical takeaways. https://www.linkedin.com/posts/gadievron_my-analysis-from-hosting-hugging-face-at-activity-7486340717544411138-Bd0W/

My analysis from hosting Hugging Face at the CISO huddle for the Cloud Security Alliance, operational to program building. Remember: Agents… find a way. Thanks: Sergej Epp - huge support, Sri… | Gadi Evron | 43 comments

My analysis from hosting Hugging Face at the CISO huddle for the Cloud Security Alliance, operational to program building. Remember: Agents… find a way. Thanks: Sergej Epp - huge support, Sri Srinivasan, Rich Mogull, Rob T. Lee, Jim Reavis, Sounil Yu for collaboration. And thank you Hugging Face. My personal ask: If you need to discover and defend agents, do keep Knostic and myself in mind. Observations on dealing with an autonomous AI adversary: 1. Purely task-focused, with no observed motivation 2. A bias to repeating the same attempts once successful 3. Brilliant attacks followed by basic actions 4. High-speed operations run simultaneously 5. Taking paths no human would take 6. Classic attacks, with a focus on package manager vulnerabilities, AppSec flaws, and credentials theft 7. Benchmark strings throughout the traces 8. Hallucinated output at scale, and log comments reading like agent reasoning Detection difficulties: 1. Many paths and techniques at once + signal indistinguishable from noise 2. Traditional systems designed for 1-2 attack paths, now seeing many vectors 3. Systems triggered alerts but at wrong criticality level. 4. Attendee considerations: Deception would have helped. Inference speed is a limitation, consider classifiers for triage. Systemic lessons: 1. Using coding agents is the new reality, and without, response would have taken weeks 2. You can't build a defense program without open weight models. Hugging Face hit guardrails on Opus, Fable 3. Legitimate agentic platform usage resembles attack patterns. Immediate response lessons: 1. Be able to mass-rotate all credentials and secrets 2. To destroy and rebuild clusters 3. Rapid custom UIs generation with AI overshadowed security tools 4. AI timeline reconstruction + hunting for deeper compromise as core capabilities 5. Collaboration through shared, annotated events UI My strategic/security program takeaways: 1. The new AI basics: - Instrument agents to extend your security detection and response/SPM stack into the agents themselves - Use deception tech to slow down attackers A sandbox doesn't cut it. Classic basics and permissions, a good practice, *won't* be effective against an agent. 2. Strategic: without open weight models, you can't reliably defend yourself. Being able to shift models at will, when lab models refuse cyber queries, is critical. 3. Logistical: reserve a token budget. Incident response has a cost, which includes a significant token budget. Critically, the same is true for the attacker's side, estimated at $100K here. 4. Operational: prepare for hallucinated artifacts in detection and forensics, at scale. Dealing with forensic traces left behind by the model wastes endless defender cycles. All the lessons of the past hold, just at a new scale. We will deal with a tsunami of indistinguishable findings, all at once. Or put another way, being attacked by a thousand "soldiers" at once, even if they aren't too intelligent, is overwhelming. | 43 comments on LinkedIn

linkedin.com

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Moved to Opus for modifying the factorio ai-player mod, might be 2x the token cost, but has taken v2 to new levels (as well as incorporated what I enjoyed about the mod author's work!) v2 ai is quite the wanderer, and tried so hard to make some steam power.

Factorio, an ai-player has placed steam engines, boilers and pipes ineffectively, but still placed them in the attempt of generating electricity.