According to the source, a newly highlighted jailbreak method can bypass many AI safety controls, creating immediate cyber-fraud risk for crypto market participants. source: public social media post Academic research has shown transferable adversarial prompts can consistently elicit restricted outputs across multiple large language models, undermining guardrails used in trading bots, exchange support, and wallet assistants. source: Carnegie Mellon University, Universal and Transferable Adversarial Attacks on Aligned Language Models (2023) Investment fraud involving crypto caused $3.94B in reported losses in 2023, underscoring the financial impact of scalable social-engineering that LLM jailbreaks can amplify. source: FBI Internet Crime Complaint Center, 2023 Annual Report Scam revenue in crypto tends to rise when prices climb, implying AI-enabled fraud pressure could intensify during bullish periods, affecting BTC and ETH market liquidity and user behavior. source: Chainalysis, Crypto Crime Report 2023 LLM-integrated apps are vulnerable to prompt injection and data exfiltration, making exchange and DeFi frontends that embed chatbots an exploitable vector traders should treat cautiously. source: Microsoft Security, Prompt Injection and Content Manipulation Risks in LLM-Integrated Applications (2024) Traders should harden OPSEC by using hardware wallets, address allowlists, minimized token approvals, and out-of-band verification for support chats to mitigate AI-assisted scams. source: CISA Shields Up (2024); NIST SP 800-63 Digital Identity Guidelines