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| Cyber |
| Chinese ‘Anti-AI’ Tactics and Techniques |
| 2025-12-01 |
| [DefenseOne] To China's war planners, AI is just another thing to deceive: The People’s Liberation Army is prepping for battles in which AIs work to distort each others' reality. A mask of darkness had fallen over the Gobi Desert training grounds at Zhurihe when the Blue Force unleashed a withering strike intended to wipe Red Force artillery off the map. Plumes rose from “destroyed” batteries as the seemingly successful fire plan took out its targets in waves. But it had all been a trap. When Blue began to shift positions to avoid counter-battery fire, exercise control called a halt—and revealed that, far from defeating the enemy, more than half of Blue’s fire units had already been destroyed. After the exercise, the Red commander explained the ruse: he had salted the range with decoy guns and what he called “professional stand-ins,” the signatures of units and troops, which not only tricked Blue’s sensors and AI-assisted targeting into shooting at phantoms, but also revealed their own firing points. It was just one example of how China’s military is building for a battlefield where humans and AI seek not just to fight, but fool each other. Under the banner of “counter-AI warfare”, the People’s Liberation Army is teaching troops to fight the model as much as the soldier. Forces are learning to alter how vehicles appear to cameras, radar, and heat sensors so the AI misidentifies them, to feed junk or poisoned data into an opponent’s pipeline, and to swamp battlefield computers with noise. Leaders are drilling their own teams to spot when their own machines are wrong. The goal is simple: make an enemy’s military AI chase phantoms and miss the real threat. The PLA conceives its counter-AI playbook as a triad that targets data, algorithms, and computing power. In May, PLA Daily described the concept in its Intelligentized Warfare Panorama series. It argued that the most reliable way to “break intelligence” is to hit all three at once. First, counter-data operations inject junk data, skew what the sensors see, slip in corrupted examples, and reshape a vehicle’s radar, heat, and visual signals with coatings and emitters that mimic another platform’s profile and even engine vibration to mislead AI-assisted ISR. Second, counter-algorithm operations take advantage of model weak spots with logic tricks and crafted inputs, confusing AIs by breaking their “reward” signals and leading them to waste time in fruitless searches. Finally, attacks on computing power include “hard-kill” kinetic and cyber strikes on data centers and links, and “soft-kill” saturation attacks that flood the battlespace with electromagnetic noise, tying down scarce computing resources and clogging decision loops. A 2024 study by PLA researchers lists soft-kill techniques such as data pollution, reversal, backdoor insertion, and adversarial attacks that manipulate machine learning models. |
| Posted by:Gloluns Turkeyneck4904 |
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| Posted by: Skidmark 2025-12-01 10:26 |