Harnessing Bugs for Defense: Rethinking Cybersecurity Strategies

Opinions expressed are solely my own and do not represent the views or opinions of my employer or clients.

I’ve been fortunate to see some of my ideas taken seriously and turned into products in the markets where I usually work. For that, I owe a huge thank you to my colleagues and managers for entertaining my “wild” ideas.

Today, I want to share a concept I’ve been tinkering with in my free time for over a decade. It never became a commercial product, but I believe it presents an interesting approach to software security.

The Motivation

I typically work in secure systems as both a developer and an architect. One of the most frustrating aspects of cybersecurity is that defenders must get everything “completely” right, while attackers only need to succeed once. It feels unfair, but that’s the reality of the job.

Software bugs are inevitable—no matter how many best practices we apply, from TDD and unit tests to CI/CD pipelines, linters, and so on. Defenders essentially get the first move, but attackers have infinite opportunities to probe for weaknesses.

That train of thought led me to a radical idea: What if we could use bugs themselves as a defensive tool? After more than 10 years of prototyping a library that implements this unusual security layer, I eventually sold the concept and its IP to an interested party.

They allowed me to share some general details, so that’s what I’ll do here.

Before diving into how “defensive bugs” fit into this, let me introduce a core concept: Collapsing Fields.

What Are Collapsing Fields?

Collapsing Fields is a defensive programming strategy where all memory allocations and related structures are interconnected in such a way that any malicious tampering causes the entire structure to “collapse,” neutralizing the attack or triggering a defensive response.

Implementation Overview

1. Unified Memory Space Critical program components (buffers, variables, control structures) are treated as a single, interconnected entity. Any change in one area can propagate checks or triggers in other parts.
2. Interconnected Traps: Canary Values: Strategically placed to detect buffer overflows, Checksum Validators: Periodically verify the integrity of the entire memory field, Linked Allocations: Modifications in one region affect others, making it easier to detect unintended changes.
3. Dynamic Collapse Mechanisms: Memory Invalidation: If an anomaly is detected, mark the entire field as corrupted and halt execution, Cleanup Triggers: Free resources, reinitialize, or reset the system to a known safe state, Logging & Alerts: Capture exploit attempts for forensic analysis.
4. Entropy Injection Randomized or variable memory patterns make it extremely difficult for attackers to predict or manipulate the system without detection.

Benefits of Collapsing Fields

• Attack Mitigation: Any exploitation in one part of the memory disrupts the entire field, effectively neutralizing or exposing the intrusion.
• Enhanced Detection: Traps like canaries and checksums provide early warnings of suspicious behavior.
• Resilience to Exploits: Memory corruption exploits become far more complex and risky to execute without triggering a collapse.
• Increased Complexity for Attackers: Malicious actors must understand the whole memory field, not just a single buffer or pointer, greatly raising the difficulty and time required for exploitation.

Going Beyond: Defensive Bugs

While I can’t detail all the additional concepts (such as “Adaptive Fields”, "Selective Collapsing" or "TL Integrity Checks") due to agreements with the entity that acquired my IP, I can explain the general idea of defensive bugs:

These are deliberately introduced “bugs” designed to interfere with attacker profiling. Automated tools that analyze the program encounter unexpected behaviors caused by these artificial flaws, slowing down or misleading the attack process. Meanwhile, defenders gain extra time to respond and gather intelligence.

Adding Neural Network Resilience

Even with detection (via Collapsing Fields) and defense (via defensive bugs), there remains a possibility that a single bit of malicious code can disable critical code paths. To address this, I experimented with integrating a small neural network that takes signals from Collapsing Fields and defensive bugs, then decides how to respond.

Neural networks can offer resilience because their “weights” and decision paths can be distributed in a way that is more difficult to corrupt in a single shot. In testing, the system successfully detected buffer overflows, heap corruptions, and Return-Oriented Programming (ROP) attacks. Additionally, the network can be retrained in a honeypot environment, adapting over time to new threats.

Conclusion

Although this approach never made it to market under me—it was deemed “too crazy” during evaluation—I hope the new owners will find a productive use for it. If properly maintained and fine-tuned, Collapsing Fields combined with defensive bugs could offer a formidable layer of security.

To those who might end up maintaining such a system, I offer my apologies in advance—debugging it will not be straightforward!