Silent Data Corruption or (SDC)'s were once just a physical phenomenon, entropy acting upon silicon. But we are now witnessing something new: the rise of AI-Software-Induced SDCs!
Software-Induced SDCs are not technically new; for example, things like Row Hammer has been known for years, but this 'hypothetical-AI-Fungus'stuff is new.
Before I dive in, I ask you, the dear reader, to try out a bad character scanner to understand this problem first hand.
For example, check out the Free Invisible Character Detector, and click on "Load Test Data" to see an example report on bad characters.
Also, for a detailed technical explanation of how AI-generated code lead to this new form of SDC, please see our companion
article: The Mechanics of AI-Generated Silent Data Corruption.
You might think, "So what? It's just a few weird characters." But here is the thing: Unicode is the source code. We tend to forget that. We treat parsers as if they are perfect, divine translators of our intent, but they are just optimized, fallible algorithms. When we feed them "homoglyphs" (characters that look identical but are different) or invisible directional overrides, we are essentially lying to the compiler about what the code is. This is the mechanism behind "Trojan Source" attacks code that looks safe to the human eye but executes a hidden, malicious payload, and despite what some say, it's not an easy fix!
Anyhow, this issue creates a supply-chain crisis: as we copy-paste from forums and PDF specifications, and increasingly rely on AI code generation, we inject these invisible artifacts into future foundational libraries.
Our analysis in "The Invisible Threat: 500x Better, But Still Lurking" shows that between 2022-2024, AI coding assistants inadvertently injected invisible Unicode characters into roughly 1 in every 20 tokens, potentially affecting 50 million repositories globally.
LLMs are then trained on this "barnacle-encrusted" code. They do not see the invisible characters as errors; they see them as high-probability tokens. Consequently, AI begins to hallucinate and insert these invisible structures into new code, code made simply out of statistical adherence to the corrupted training data!
This creates a feedback loop of "unattended code," where the fungus spreads from the training set to the output, passes through the compiler (which reads the raw bytes, not the rendered text), and embeds itself into production systems.
This danger is amplified in low-level environments. Consider C++ and build systems like CMake. These tools are the bedrock of our infrastructure, yet they are incredibly sensitive. A single invisible character in a CMake rule file can be interpreted as a delimiter, silently altering how the entire project is built. The compiler doesn't "see" the mistake; it optimizes around it. This means the "fungus", these invisible errors, can bypass high-level checks and bake themselves directly into the binary. We are moving from an era of accidental contamination to a potential future of "homoglyph spoofing" and weaponized ambiguity, all hidden in the invisible whitespace of our most trusted systems.
Further Hypothetical Issues
Now, this is an extremely speculative idea, it's hypothetical but maybe-plausible in the near future, but let's imagine where this could lead.
Hardware SDCs are stochastic and rare. Most can be fixed with a simple check-sum correction. However, AI-SDCs can often pass through check-sum correction and most importantly this corruption is systemic and cumulative. The fact that it is both rare and cumulative is a key aspect of this problem that makes it difficult to diagnose and isolate.
This kind of issue thrives in the gaps between parsers interpreting assembly code and "optimized" low-level languages, such as C. However, the gap between assembly code and high-level language interpretations, such as JavaScript, is much worse.
Now, back to the hypothetical 'life cycle': It begins with invisible Unicode characters, such as the Zero Width Space (U+200B) or the Right-to-Left Override (U+202E), which can be used in Trojan source attacks. In our controversial metaphor, these bad characters are the spores. When developers delete code in modern IDEs, they often only remove the visible glyphs, leaving behind these invisible "ghost characters" or "zombie bytes."
To the human eye, the code looks clean, but the compiler can sometimes struggle to distinguish it from normal code. And when that happens... it can be bad. Even one of these bad characters in the wrong place can cause logic errors. For example, variables can bifurcate into entities that look identical but are distinct, which can be extremely difficult to prevent or test for.
In this phenomenon, it is not the code that breaks, but it might be, in a way, 'alive'.
To understand this, we have to look at the "acasual" emergence of self-replicating risks, deeply analyzed by science communicator Anton Petrov in his video Did Google Researchers Just Create a Self-Replicating Computer Life Form?.
Petrov breaks down a groundbreaking paper by Google Research (Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction), which simulated a "digital primordial soup." The researchers used a modified version of the esoteric programming language Brainfuck (known for its extreme minimalism and sensitivity to single-character changes) to run a simulation of random programs interacting with each other.
As Petrov highlights, the results were shocking:
He states there is no Fitness Landscape, as in, the system had no goal, no "survival of the fittest" instructions, and no reward for replication. It all came from pure random noise.
Inevitability: Petrov notes that "if anything is occurring randomly, it will either accomplish nothing, destroy itself, or self-replicate." In a chaotic environment, self-replication is the only stability.
The DNS Network as a Primordial Soup
Let's take this hypothetical idea further, we must then ask: What is the global DNS network if not a massive, chaotic, digital primordial soup?
If the "AI Fungus" of invisible characters ($U+200B$, $U+202E$) and homoglyphs continues to build up in DNS Servers, root servers, text records and routing tables, we provide the exact substrate described in Petrov's analysis. An AI agent, managing DNS entries, might hallucinate a string of invisible characters that happens to form a valid instruction in a permissive interpreting environment.
If that string causes the record to be cached longer, or copied more frequently by other agents, it becomes a "self-replicator" in the Darwinian sense. We could see the acasual birth of a "mycelial" network layer a shadow DNS composed of homoglyph domains (e.g., Cyrillic 'a' replacing Latin 'a') and self-propagating invisible TXT records. This could be harmless or "odd craziness" of unresolvable domains and routing loops. But it could be the first instance of a metaphorical 'metabolic activity of a digital organism' made of our own discarded, invisible syntax.
I use DNS as an example, but I think it's actually unlikely to happen there first, if ever. It's just the first thing that came to mind. In the Google paper, they also state that languages with more complex syntax than "Brainfuck" are less likely to experience this kind of emergence.
Key References:
- Cosmic ray SDC background: Wikipedia - Soft error
- Video Analysis: Did Google Researchers Just Create a Self-Replicating Computer Life Form? (Anton Petrov)
- Primary Paper: Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction (Agüera y Arcas et al.)
- Invisible Character Threats: Bad Character Scanner Blog - Articles on Trojan Source and invisible Unicode vulnerabilities.
- Homoglyph & DNS Risks: Trojan Source: Invisible Vulnerabilities (Boucher & Anderson)
This Part is an Ad:
Bad Character Scanner™ (BCS) is a tool designed to detect and prevent SDC in AI-generated code and text. BCS can scan for most types of SDCs before they have a chance to turn into unexpected behaviours (or self-replicating headaches).