Anchoring the '15th Five-Year Plan': When Cyber Security and Information Technology Meet 'AI Poisoning' and 'Model Inversion', How Will the Focus of Defense Shift?

At the starting point of the Cyber Power Strategy moving towards the "15th Five-Year Plan", a "quiet" paradigm shift is taking place.

If we were accustomed to "building walls to keep people out" at the edge of the digital world over the past decade, now, our adversaries have quietly turned their sights toward your data kernel and algorithmic logic. This is not just a technological transition, but a "physical shift" concerning the essence of security: the focus of defense is irreversibly drifting from the outer perimeter to the algorithmic kernel.

"Genetic Pollution" at the Cognitive Level: Shifting from Entrance Security Inspection to Kernel Pressure Verification

The "data poisoning" mentioned by Director Zhuang Rongwen is essentially a "trust hijacking" of the learning mechanism of artificial intelligence.

The traditional logic of security protection is: intercepting hackers and filtering viruses. However, masters of "poisoning" neither destroy code nor generate abnormal traffic; instead, before the model is fully trained, they insert "dirty stuff" into the training data.

This is akin to planting an invisible hazard within the genes. Normally, the model looks smart and clever, but once a specific trigger signal is activated, it instantly develops a cognitive bias, leading you astray.

Therefore, the focus of defense must shift: relying solely on security inspection at the entrance is no longer enough; we must conduct pressure verification. That is, simulating this kind of poisoning scenario to see how much sanity your model can still maintain when subjected to interference—this is called kernel immunity.

"Gentle Penetration" under Legitimate Paths: Shifting from Access Control to Leakage Risk Measurement

If poisoning is injecting bias inward, then "model inversion reasoning" is utilizing legitimate paths to drain assets outward.

Instead of climbing through windows or picking locks, they sit in the lobby and continuously solicit information through legitimate APIs. Based on the feedback provided by the model, they trace back step by step—like playing a jigsaw puzzle—to reconstruct your training set's sensitive information or even core parameters.

At this point, the focus of defense must shift again: traditional authority control is not very effective here. We must measure the leakage risk, which means verifying to what extent the success rate of the opponent attempting to reconstruct secrets through solicitation has been diluted after we have introduced obfuscation interference.

Penetrating the Algorithmic Black Box: Shifting from "Piling up Protections" to "Continuous Practical Verification"

The core of the non-traditional security challenges mentioned in the "15th Five-Year Plan" lies in the blurring of security boundaries. When AI evolves from an auxiliary tool into a core asset, traditional protection equipment often only provides a "tactile security," which is usually lagging behind in the face of highly covert algorithmic confrontations.

True security comes from the visibility of confrontational scenarios. The ultimate shift of the defense focus points to a continuous closed loop of confrontational experiments: transforming scattered threats into visible actual measurement scenarios, shifting security efforts from "buying equipment" to "conducting verification." Through extreme stress testing, invisible algorithmic risks are converted into definitive security value indicators.

This shift from static defense to dynamic quantitative assessment is not only a leap in the technological path, but also an inevitable choice for cybersecurity to penetrate into "deep waters" in the next five years.