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AI Tools Now Use Radar To Wiretap Your Phone From 10 Feet Away
How New Technology Can Eavesdrop on Your Conversations
Most of us assume that our phone calls are private, at least between us and the person on the other end. But what if someone across the room—or even across the street—could pick up fragments of your conversation without ever touching your phone?
Thanks to the merging of artificial intelligence and radar technology, that unsettling possibility is no longer the stuff of science fiction.
Researchers at Penn State University have built a proof-of-concept system that can remotely “listen” to phone conversations by reading microscopic vibrations from the device itself. Using millimeter-wave radar—the same technology behind 5G networks and self-driving car sensors—paired with a modified AI speech recognition model, the system can transcribe conversations from up to ten feet away with about 60 percent accuracy.
That’s not perfect, but it’s enough to suggest that the devices we trust most could soon betray us in ways we’ve never imagined.
A Century of Creative Eavesdropping
The Penn State system is only the latest chapter in a long history of surveillance methods designed to exploit vulnerabilities in communication.
The Penn State system is only the latest chapter in a long history of surveillance methods designed to exploit vulnerabilities in communication.
In the early 1900s, wiretaps gave police—and sometimes criminals—direct access to telephone lines. During the Cold War, intelligence agencies experimented with laser microphones, bouncing light off windows to capture the vibrations of voices inside a room. In the 2000s, cell phone security flaws opened the door for hackers and government agencies to track calls with stingray devices.
And in the past decade, researchers have uncovered “side-channel attacks”—ways of extracting private information from indirect signals. These include the sound of someone typing on a keyboard, fluctuations in a computer’s power consumption, or even the faint glow patterns on a monitor.
The Penn State breakthrough belongs to this last category. It’s not about hacking a network or breaking into apps. Instead, it uses the unintended vibrations that ripple through a phone whenever sound emerges from the earpiece.
From Vibrations to Words
Every time a smartphone speaker plays a voice, it produces sound waves and tiny surface vibrations. These movements are invisible to the human eye, but a sensitive radar can detect them.
In their experiment, the Penn State team placed a millimeter-wave radar sensor three meters away from a phone playing a call. The radar measured the subtle fluctuations caused by speech. On their own, the radar signals were garbled and indecipherable. That’s where artificial intelligence entered the picture.
The researchers modified Whisper, an open-source AI speech recognition model, to process radar signals instead of audio recordings. Instead of retraining the entire model, they used a technique that updated only 1 percent of its parameters. This allowed Whisper to adapt quickly and efficiently to a radically different type of input.
The result was striking. The AI could transcribe live conversations with about 60 percent accuracy, drawing from a vocabulary of 10,000 words. That may sound error-prone, but it’s an enormous leap from the team’s earlier 2022 project, which could recognize only ten preset words.
Why Partial Accuracy Still Matters
Skeptics may wonder whether 60 percent accuracy is good enough to matter. But as the researchers point out, even partial transcripts can reveal highly sensitive information.
Catching just a few words—like a name, an address, or a key number—can be enough to identify a person, place, or account. Fragments of credit card numbers, banking details, or medical references could be pieced together. And over time, context allows missing words to be filled in.
Think of lip readers. They often only catch 30 to 40 percent of words, yet can infer entire conversations through context. An AI-powered radar system could do the same, stitching together incomplete pieces into a surprisingly accurate narrative of what was said.
Voices from the Research Team
The researchers themselves emphasize that their goal was not to build a spy tool but to raise awareness about potential vulnerabilities.
“When we talk on a cellphone, we tend to ignore the vibrations that ripple through the device,” explained doctoral candidate Suryoday Basak, the study’s first author. “But if those vibrations can be measured remotely and paired with machine learning, it becomes possible to reconstruct conversations. Our goal is to demonstrate this risk so the public—and the tech industry—won’t be caught off guard.”
Associate professor Mahanth Gowda, who co-authored the study, highlighted how far the research has come in just a few years. “Our 2022 system was like training wheels—it worked on ten preset words. Now we’re dealing with actual flowing speech. Yes, it’s error-prone, but the jump from a dictionary of ten words to transcribing thousands marks a huge advancement. Even partial matches can have serious implications when security is at stake.”
A New Category of Privacy Risk
The implications are unsettling. Privacy risks don’t just come from malware, phishing scams, or compromised networks. They can also stem from the very physical properties of our devices.
Your smartphone wasn’t designed with the assumption that its surface vibrations could be treated as a backdoor microphone. But that’s exactly what this proof-of-concept demonstrates.
Possible dangers include corporate espionage, where competitors might intercept details of sensitive business calls; threats to journalists, who could unknowingly expose their sources; and risks to personal security, where medical consultations or family conversations might be overheard.
Although the research took place in controlled conditions, the study highlights an attack vector that exists today—and could easily be exploited tomorrow. As radar sensors become cheaper and AI continues to improve, the barriers to misuse will only shrink.
Can Anything Be Done?
The good news is that defensive strategies are possible, though they require foresight.
Smartphone hardware could be redesigned to reduce vibration leakage from speakers. Software might add background “noise” signals that confuse radar while leaving audio unaffected for the user. Special cases or materials could block or distort radar waves. And in high-security environments, policies could restrict phone use altogether.
The researchers stress that prevention is the best approach. Once adversaries begin to weaponize these methods, defenses will be harder to implement.
The Larger Surveillance Landscape
The Penn State project is part of a bigger trend: the fusion of artificial intelligence with advanced sensing technologies. Together, these tools can exploit weak signals once thought too noisy to be useful.
Other examples include AI models that reconstruct typed text from keystroke sounds, systems that infer passwords from thermal traces left on keyboards, and deep learning programs that predict speech from silent lip movements in videos.
Each new advance shows how information leaks in ways traditional security systems rarely anticipate. With AI growing more powerful, these “side channels” are rapidly shifting from theoretical curiosities to practical threats.
More Warning Than Weapon—For Now
It’s worth emphasizing that the Penn State system is not yet a ready-made spy kit. It requires specialized equipment, careful positioning, and limited distance. Sixty percent accuracy, while impressive, still leaves gaps that would challenge most eavesdroppers.
But theories often become realities. Wiretaps once seemed exotic. So did hacking wireless signals. Today both are part of everyday security concerns. The same may eventually be true of AI-powered radar eavesdropping.
The researchers’ message is clear: awareness is essential. By exposing the possibility now, they hope to encourage defenses before malicious actors put the idea into practice.
The Final Takeaway
Your phone is more than just a tool for communication. Under the right conditions, it can also be a beacon, silently radiating clues about what’s happening inside it. By marrying radar technology with artificial intelligence, Penn State researchers have shown that those clues can be transformed into words.
This isn’t a call to panic. It’s a reminder that privacy must constantly be defended—and that new vulnerabilities often emerge from the most unexpected places. The future of eavesdropping may not involve hacked apps or compromised networks, but the very physics of the devices we hold in our hands.
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