According to @GoogleDeepMind, VP of Security Four Flynn joins host @FryRsquared in a new podcast episode outlining how newer AI models are being leveraged to defend against increasingly sophisticated cyber attacks, with Part 1 now available (source: Google DeepMind, X post, Oct 16, 2025). According to @GoogleDeepMind, the episode maps out key segments including Project Aurora (02:00), the defender’s dilemma (20:48), zero day vulnerabilities (21:22), the kill chain (23:49), LLM vulnerabilities (25:39), malware, polymorphism and prompt injection (27:00), Big Sleep (37:00), and using AI to fix vulnerabilities via CodeMender (45:00) (source: Google DeepMind, X post, Oct 16, 2025). According to @GoogleDeepMind, this lineup specifically surfaces LLM vulnerabilities, prompt injection, zero-day exploits, and AI-driven remediation, topics directly tied to security considerations for AI-integrated systems used across finance and crypto infrastructure (source: Google DeepMind, X post, Oct 16, 2025). According to @GoogleDeepMind, no specific cryptocurrencies or market metrics are cited in the post, but the episode’s focus areas align with threat vectors relevant to exchanges, wallets, and DeFi platforms that increasingly deploy AI tooling (source: Google DeepMind, X post, Oct 16, 2025).

According to @AnthropicAI, browser use introduces safety challenges for AI models—especially prompt injection—and the company has launched a pilot to strengthen existing defenses in Claude’s browsing capability (Source: @AnthropicAI, Aug 26, 2025). According to @AnthropicAI, the announcement shares that safety measures already exist and the pilot aims to improve them, while providing no timelines, metrics, product release details, or any cryptocurrency/market impact disclosures (Source: @AnthropicAI, Aug 26, 2025).

According to Berkeley AI Research, their latest blog post discusses innovative techniques to defend against prompt injection attacks using Structured Queries (StruQ) and Preference Optimization (SecAlign). These methods, led by Sizhe Chen and Julien Piet, aim to enhance AI model security by structuring queries to prevent unauthorized data manipulation and optimizing preferences to align with secure protocols.