X-Industry

A recent report by Salt Security highlights a critical warning: without proper Application Programming Interface (API) discovery, governance, and security, the very technology meant to drive smarter customer engagement could open the door to cyber-attacks or data leakage.  The research also reveals an increasing trust gap between businesses that deploy agentic AI for external communications and consumers who are wary of sharing personal information due to security concerns.

Because APIs power AI

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