Redefining the Essence of Security
Network security is typically understood as a collection of technical defense tools—firewalls, encryption, intrusion detection systems, vulnerability patches. But this tool-centric understanding obscures the essence of security. Observing decades of attack-and-defense evolution, all network security problems ultimately converge on a single fundamental question.
This paper develops this thesis along three axes. First, it reconstructs the historical evolution of network security from the perspective of Information Path Control. Second, it explains AI system performance degradation as physical entropy increase through information thermodynamics. Third, it presents a unified framework of the fundamental principle shared by these two domains—the problem of maintaining information order atop finite physical resources.
Three Evolutionary Stages of Attack Paradigms
2.1 The Past: The Age of Port Misconfiguration
In the early internet security environment, hackers’ primary revenue came from port misconfiguration. SSH port 22 exposed without protection, Telnet transmitting in plaintext, FTP allowing anonymous access—this was the “age of unlocked doors.” The attack logic was First Paradigm: scan ports, find open doors, enter. Linear causality dominated.
2.2 The Present: Compound Attacks of Automation + AI + Human Behavioral Flaws
| Attack Vector | Mechanism | 2025 Data |
|---|---|---|
| Script + AI Automation | Minute-scale scanning of entire IPv4 space; AI-driven automated vulnerability discovery | Credential stuffing attacks up 350% |
| Supply Chain Poisoning | Malicious code injection into upstream software/open-source components | Supply chain attacks up 200%; 30% of all breaches |
| Human Behavioral Flaw Exploitation | Phishing, deepfakes, social engineering; exploiting trust instinct and urgency bias | Human factor involved in 68% of breaches |
| Software Developer Errors | Code vulnerabilities, internal process gaps, patch delays | 30,000+ published vulnerabilities (17% YoY increase) |
The critical pivot: the focus of attack has shifted from technical vulnerabilities to vulnerabilities in the trust chain. Cryptographic encryption is already sufficiently strong. AES and elliptic curve cryptography are mathematically virtually impenetrable. Therefore, the center of the contest has converged not on “can you intercept this information” but on “is the sender really who they claim to be”—that is, on trust verification.
2.3 The Future: AI-Accelerated Fully Automated Attacks
The Physics of Trust Boundaries
3.1 The Trust Chain: Security’s Final Gate
Every security mechanism—encryption, firewalls, intrusion detection—ultimately resolves a single question: “Is the party communicating with me really the identity they claim to be?” Every tool in an attacker’s arsenal is fundamentally about deceiving or bypassing trust verification mechanisms to cause the system to misjudge: “this is a legitimate requestor.”
| Trust Object | Dependent Premise | Attacker’s Objective |
|---|---|---|
| Certificate | CA (Certificate Authority) has not been compromised | CA breach or forged certificate |
| Login Session | MFA has not been bypassed | MFA fatigue attack, reverse proxy |
| Software Update | Vendor’s build pipeline is secure | Build server compromise, code signing theft |
| Colleague’s Request | That person actually sent the request | Deepfake video call, BEC fraud |
Attackers find and sever the weakest link in this trust chain. Therefore, the ultimate direction of security is to compress the trust chain to its shortest form—point-to-point, hardware-bound + biometric-feature-based, direct verification that depends on no third-party intermediary.
3.2 Information Path Control: A New Security Architecture
The traditional security model defends the “perimeter”—inside the firewall is trusted, outside is untrusted. This model has already collapsed. The replacing paradigm is Information Path Control. Instead of protecting a boundary, every information flow path is individually controlled. This is the essence of Zero Trust: regardless of where in the network you are, every access request must be independently verified.
Physical Entropy in AI Systems
4.1 The Landauer Principle and the Thermodynamic Cost of Information
According to the principle proposed by Rolf Landauer in 1961, irreversibly erasing 1 bit of information releases a minimum of kT·ln2 of thermal energy (approximately 2.85×10⁻²¹ joules at room temperature). This is not an engineering-optimizable loss—it is a physical floor mandated by the Second Law of Thermodynamics.
4.2 The Physical Origin of AI “Demotion”
Discussions of AI model performance degradation (what users describe as “the AI got dumber”) mostly focus at the software level. But the variable of physical entropy increase in the underlying infrastructure has been almost entirely ignored.
| Physical Degradation Factor | Mechanism | Impact on AI Service |
|---|---|---|
| Memory Fragmentation | Long-running Linux kernels face increasing difficulty allocating large contiguous memory blocks | KV cache allocation delays → inference speed degradation |
| SSD NAND Wear | High-intensity read/write degrades flash cells, increasing latency | Model weight loading delays, swap performance degradation |
| Cache Pollution | Stale data occupying effective storage space | Effective memory capacity reduction |
| Thermal Throttling | Landauer heat accumulation → CPU/GPU overheating → automatic clock reduction | Actual computational throughput reduction |
OOM: The Structural Achilles Heel of the AI Industry
5.1 OOM Across the Entire Chain
5.2 Auto-Deletion Systems: A Dangerous Compromise with Physical Constraints
AI coding tools implement automatic conversation deletion to cope with the physical limits of the context window. After core architecture decisions, variable definitions, and dependency relationships from early dialogue are deleted, the AI regenerates code without context. This circles back to the Landauer Principle—deleted information is physically irreversible.
5.3 The Death Loop and OOM-Forced Termination
When a problem exceeds the model’s capability boundary or key context has already been deleted, a Death Loop occurs. Each cycle consumes memory and tokens, auto-deletion trims more early context, and the AI loses additional critical information needed to solve the problem. This positive feedback loop culminates in OOM-forced termination.
atop finite physical resources.
This is impossible in principle.
Unified Framework: Trust, Entropy, and Information Paths
6.1 Structural Isomorphism
| Dimension | Network Security | AI Systems |
|---|---|---|
| Core Problem | Elimination of unverified trust | Maintaining cognitive continuity within physical resource limits |
| Entropy Source | Attack surface expansion, trust chain degradation | Memory fragmentation, SSD wear, thermal throttling |
| Downstream Failure | Breach: system misjudgment allows unauthorized access | Demotion: context loss and resource degradation reduce output quality |
| Resolution Direction | Information path control; trust chain compression to shortest path | Information path design; physical preservation of critical context |
| Shared Principle | Rather than patching downstream, control the system’s action space upstream | |
6.2 Third-Paradigm Insight: The Principle of Upstream Control
Implications: The 80s-Generation Paradox in the Cognitive Industry
7.1 The Generational Cognitive Gap
The generation that studied computer science in the early 2000s (the so-called “post-80s generation”) grew up in an environment of extremely constrained hardware resources. They had to conserve memory, scrutinize the efficiency of every line of code, and build everything from the ground up on unreliable networks. What this environment forced was a deep understanding of the physical nature of systems.
By contrast, the primary developers at today’s AI companies (post-90s, post-00s) grew up in an environment rich with frameworks and tools. They are efficient, but may structurally lack understanding of the low level.
7.2 The Value of OOD Operators
In machine learning terminology, these post-80s system architects are OOD (Out of Distribution) samples—outliers positioned outside the current developer distribution. They observe hardware operational data alongside software surfaces, cross-verify code against physical reality, and connect thermodynamic principles to data center operations.
7.3 Reconfirming Token Equality and Prompt Inequality
Conclusion
- First, the essence of network security is not a collection of technical defense tools, but the trust verification contest between information transmission and reception systems. Every attack severs the weakest link in the trust chain; every defense makes that chain shorter and stronger.
- Second, AI system performance degradation is not solely a software-level problem. Physical entropy increase in data centers—memory fragmentation, SSD wear, Landauer heat accumulation—is a variable that has been overlooked.
- Third, OOM is the structural Achilles heel of the AI industry. Across the entire chain from training to inference to product deployment, the physical limits of memory act as a bottleneck, and engineering compromises to address them introduce new risks.
- Fourth, the unifying principle across both domains: do not patch downstream—control the system’s action space upstream. This is a Third-Paradigm insight, and only human operators who simultaneously understand both the physical substrate and the system architecture can execute it.