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The Hoxhunt 2025 Cyber Threat Intelligence Report delivers a sobering message for security professionals: the most dangerous threats are no longer the most obvious ones.  As 2026 approaches, enterprises are no longer fighting clumsy, error-riddled bulk spam; they are facing a quiet revolution where sophisticated, convincing attacks blend seamlessly into daily workflows, fueled by AI and advanced token-theft toolkits.

See:  https://hoxhunt.com/guide/threat-intelligence-report

The report, based on

A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request.  Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions.  Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning.  Rather than directly asking the model to generate in

A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request.  Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions.  Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning.  Rather than directly asking the model to generate in

A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request.  Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions.  Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning.  Rather than directly asking the model to generate in

A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request.  Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions.  Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning.  Rather than directly asking the model to generate in

A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request.  Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions.  Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning.  Rather than directly asking the model to generate in

A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request.  Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions.  Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning.  Rather than directly asking the model to generate in

A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request.  Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions.  Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning.  Rather than directly asking the model to generate in

A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request.  Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions.  Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning.  Rather than directly asking the model to generate in

A proof-of-concept attack detailed by Neural Trust demonstrates how bad actors can manipulate LLMs into producing prohibited content without issuing an explicitly harmful request.  Named "Echo Chamber," the exploit uses a chain of subtle prompts to bypass existing safety guardrails by manipulating the model's emotional tone and contextual assumptions.  Developed by Neural Trust researcher Ahmad Alobaid, the attack hinges on context poisoning.  Rather than directly asking the model to generate in