Amazon has prevented more than 1,800 suspected North Korean operatives from securing employment since April 2024, as the Pyongyang regime continues efforts to place IT workers in remote roles at Western companies to generate revenue for the regime. Amazon's Senior Vice President and Chief Security Officer, Stephen Schmidt, revealed the figures in a LinkedIn post, noting a 27% quarter-on-quarter increase in the number of detected DPRK-affiliated applications this year. The aim, he said, is for o
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Remote Access Trojans, also known as RATs, have been around for years, although their prevalence in the market has surged recently. RATs are digital skeleton keys, giving an attacker remote control over a system, often without the user ever knowing. This kind of access often starts with someone clicking a malicious link or opening a rogue attachment in a phishing email or messaging app. From there, the attacker can move laterally, steal data, monitor activity, or trigger ransomware.
RATs have
This reads like a James Bond movie. Chinese scientists have been devising sophisticated ways to destroy Elon Musk’s Starlink satellite network, including using stealth submarines fitted with space-shooting lasers, amid Chinese government fears that the technology poses a risk to its strategic interests. As of 2025, China's military, specifically the People's Liberation Army Navy (PLAN), is estimated to operate between 65 and 75 submarines. This fleet includes both nuclear-powered and diesel-el
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
