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US Tells Election Officials to Be on Guard Against AI-Powered Tricks Ahead of Midterms
- On Friday, the FBI and CISA warned the American public to remain vigilant against AI-powered tricks ahead of next month's midterm elections, highlighting sophisticated tactics employed by malicious cyber criminals.
- The alert noted that "super intelligence," a Trump-approved term for AI, is allowing hackers to sharpen rogue emails and text messages, making them appear more convincing to potential victims.
- Fraudsters are deploying various schemes including fake PACs that collect donations, merchandise scams, and investment pools, often mimicking Campaign entity names and slogans to deceive supporters, the FBI reports.
- Scammers are using phishing campaigns, sending text messages claiming recipients are not registered to vote to solicit personal data; the FBI advises verifying all political organizations via official government Elections websites.
- While Texas Secretary of State Alicia Pierce confirmed a registration application backlog is cleared, Scammers may still leverage the incident to create urgency in deceptive phishing communications targeting Texans.
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US tells election officials to be on guard against AI-powered tricks ...
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Uncle Scam Wants You … to cough up your cash and personal data, FBI warns ahead of midterms | #cybercrime | #infosec - National Cyber Security Consulting
cyber-crime The inevitable election-themed cyberattacks are whirring again, as the feds issue PSA about the ones to watch out for The FBI is once again warning US citizens to be on the lookout for the scams that are so often rife around election season.With November’s midterms fast approaching, the FBI said scammers are known to […] Thank you for subscribing to our RSS feed!
The FBI and CISA warn that cybercriminals could use fraudulent text messages and emails, more convincing thanks to AI, to try to steal sensitive data before the U.S. legislative elections.
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Total News Sources13
Leaning Left0Leaning Right2Center4Last UpdatedBias Distribution67% Center
Bias Distribution
- 67% of the sources are Center
67% Center
C 67%
R 33%
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