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 @timnitGebru, there is a persistent issue with large language model (LLM) vulnerabilities that companies have not fully addressed, as shown in a recent post (Twitter, June 3, 2025). Timnit Gebru highlights that despite ongoing security concerns, organizations are not sufficiently aware or proactive in red teaming and patching LLMs. For crypto traders, this lack of robust AI security exposes blockchain projects and crypto exchanges—many of which rely on LLMs for trading bots, customer support, and smart contract analysis—to increased risk of exploits and manipulation, potentially causing market volatility and undermining trust in automated crypto solutions (source: @timnitGebru, Twitter, June 3, 2025).