How AI could make it harder for governments to use hacking tools
Earlier in August, cryptography professor Matthew Green wrote a controversial thread on X and a longer blog post that went viral within the cybersecurity community. Green, who has long been a close observer of the debate around the use of hacking tools by governments to fight crime and the need for strong encryption to protect the privacy of innocent people, posited a provocative thought: What if AI makes bugs so scarce that law enforcement and intelligence agencies are unable to lawfully hack criminals anymore?
Why It Matters
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Key Facts
- Fact 1: The concept of “going dark” was popularized in 2014 at a time when then-FBI director James Comey warned that encryption could hamper authorities from being able to listen in on conversations or access data on devices.
- Fact 2: Hamid Kashfi, who is the founder of offensive security firm DarkCell and who also works at the AI cybersecurity startup Xbow, said that “for every AI found and reported bug out there, there are probably 20 that are not reported.” Kashfi explained that researchers who do not want to report bugs to vendors can still find complex and valuable bugs.
Earlier in August, cryptography professor Matthew Green wrote a controversial thread on X and a longer blog post that went viral within the cybersecurity community. Green, who has long been a close observer of the debate around the use of hacking tools by governments to fight crime and the need for strong encryption to protect the privacy of innocent people, posited a provocative thought: What if AI makes bugs so scarce that law enforcement and intelligence agencies are unable to lawfully hack criminals anymore?
“I’m concerned that AI is going to make software much too secure,” Green wrote, warning that the U.S. government may lose access to security flaws to hack into targets they need to surveil as companies patch an unprecedented volume of bugs. Law enforcement have long claimed that encryption made it difficult to catch criminals and terrorists.
(Original synthesis pending human/AI review — generated by the stub provider by selecting real sentences from the source material, not by writing new analysis or commentary.)
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