Garbage In, Slop Out... Why AI Slop Is a Trust Crisis (and a Security Problem)

Proceed with caution. Source of truth unknown.

For decades, technologists lived by a simple rule... Garbage In, Garbage Out (GIGO). If your inputs are messy, biased, incomplete, or wrong, your outputs will be too.

Generative AI didn’t retire that rule. It industrialized it.

We’re now living in the era of Garbage In, Slop Out, where AI can rapidly produce convincing content at scale, even when the underlying “truth” is unclear, contaminated, or simply fabricated. Merriam-Webster made the point mainstream by defining slop as “digital content of low quality that is produced usually in quantity by means of artificial intelligence.”

So what exactly is “AI slop,” why is it spreading, and why should anyone in leadership, especially security, risk, compliance, product, or operations... care? Because “slop” isn’t just annoying. It’s an early warning sign of a bigger issue, the degradation of digital trust.

What “AI slop” really is (and why it’s different from normal “bad content”)

AI slop is not merely “bad writing” or “cheap graphics.” It’s a specific combination of -->

• Low-effort generation

• High-volume distribution

• Monetization incentives (ads, SEO, affiliate spam, engagement farming)

• Weak accountability (no author credibility, no sourcing, no verification)

Google has openly targeted “scaled content abuse", mass-produced low-quality or unoriginal content designed to manipulate rankings, because the flood degrades search quality. That’s a big tell, when search engines adjust policy and algorithms to defend against a wave, the wave is real.

My new warning label for modern AI... “Proceed with caution. Source of truth unknown.”

Most people assume AI answers come from some stable library of facts. They don’t. A model’s response is an output of -->

• what it was trained on,

• what it was fine-tuned on,

• what it retrieved (if using RAG),

• and what it inferred statistically.

Even with retrieval-augmented generation (RAG), which is designed to ground an answer in external sources, the risk doesn’t disappear, especially in high-stakes domains. Stanford researchers have specifically challenged “hallucination-free” claims and tested reliability in legal RAG settings, showing that retrieval doesn’t magically eliminate error. So the modern operating posture becomes...

Proceed with caution. Source of truth unknown.

That doesn’t mean “AI is useless.” It means AI needs controls.

The slop feedback loop --> when the internet becomes training data… and training data becomes the internet

Here’s the part that should make every technologist think deeply...

1. Humans publish content online (good, bad, and ugly)

2. AI models train on that content

3. AI generates new content

4. That AI content gets published online at massive scale

5. Future models train on that content too

This is how we get a self-feeding loop of synthetic content. It’s not only repetitive, it’s erosive. The “average” quality of what’s available for learning (by humans and machines) drops. And when that loop is monetized, it’s not an accident, it’s a business model. Marketwatch recently called out that “AI slop is taking over the internet,” framing it as a persistent feature of the content economy, not a short-term trend.

Why I believe AI slop is also a cybersecurity issue

Most people discuss AI slop as a culture problem, annoying feeds, junky videos, spammy blogs. Security people should see it differently... AI slop is an attack surface.

1) Misinformation at scale becomes operational risk

When your staff can’t tell what’s real -->

• incident response slows down

• decision-making quality drops

• executive trust erodes

• fraud becomes easier

And unlike yesterday’s spam, slop can be “polished enough” to pass a casual glance.

2) Data poisoning... “garbage in” can be deliberate

Data poisoning is not hypothetical. It’s a known adversarial technique, injecting malicious or misleading data into training pipelines to bias behavior, degrade performance, or plant backdoors. NIST’s adversarial ML taxonomy includes data poisoning as a recognized class of attack. In 2025, research surveys explicitly expanded discussion to poisoning risks in deep learning and even LLM contexts. Now connect the dots:

• If the open web is full of slop…

• and models learn from the open web…

• then the opportunity to “shape the model’s reality” expands.

Slop would be the 'fog', and Poison becomes the 'dagger'.

3) RAG poisoning... your “source of truth” can be compromised

A lot of organizations respond to hallucinations with RAG, “We’ll just ground the model in our internal documents.” Good move, if your retrieval corpus is clean. But if attackers can insert documents into your knowledge base, manipulate public sources your RAG indexes, or exploit weak content governance…then you’ve built a highly scalable misinformation engine that speaks with the confidence of “internal truth.” That’s not just a model risk, that’s a governance failure.

The real ramifications... the slow corrosion of reality

AI slop creates a world where authenticity becomes expensive, verification becomes mandatory, and trust becomes a premium feature. We'll start seeing a two-tier internet -->

1. “Free” content that is abundant but unreliable

2. “Paid/verified” content that is scarce but trustworthy

I've seen this already happening in pockets, and it will shape the next phase of business, media, and security. Even standards bodies are leaning into provenance and authenticity tooling. The Coalition for Content Provenance and Authenticity (C2PA) exists specifically to standardize ways to attach provenance metadata (“Content Credentials”) to media so consumers can assess origin and edits. C2PA+1 NIST has also published work focused on reducing risks posed by synthetic content, including provenance and watermarking approaches. This is the direction of travel... trust signals, provenance, verification.

“Garbage in, slop out” in the enterprise... what this breaks first

If you’re in a business, slop doesn’t show up as “weird memes.” It shows up as:

Policy & compliance drift

People paste AI-generated policy language into official documents, confident, generic, and sometimes wrong. That can create audit risk fast.

Vendor management confusion

Procurement and risk reviews get polluted by AI-written “evidence” and marketing claims. Without verification, you’re approving controls based on narrative, not proof.

Security operations signal loss

SOC analysts already fight alert fatigue. Add slop content, bad threat intel write-ups, auto-generated IOC lists, hallucinated CVE narratives, and you get analysis fatigue too.

Executive decision risk

At the leadership level, “source of truth unknown” isn’t an academic concern. It’s how bad decisions happen confidently.

Defending against slop (without becoming anti-AI)

1) Require citations like you require MFA

If an AI-generated claim impacts operations, security, money, or reputation...

• demand sources

• verify sources

• log sources

No sources? Treat it as untrusted.

2) Build a “Source of Truth” hierarchy

• authoritative internal docs

• vetted external sources

• everything else

Then enforce it in workflows and tooling.

3) Treat RAG like a production dependency (because that's what it is)

For any internal knowledge base...

• control ingestion

• approve sources

• monitor for tampering

• version content

• log retrieval and outputs

RAG isn’t magic, it’s a data pipeline. And all data pipelines need security.

4) Analyze your AI outputs

NIST’s AI Risk Management guidance emphasizes structured approaches to AI risks, this is the mindset shift organizations need. Security teams should practice...

• prompt injection tests

• retrieval manipulation tests

• poisoning-style tests in the knowledge base

• “high confidence / low evidence” detection

5) Use provenance where possible

For media workflows, provenance standards like C2PA aim to make origin and edit history easier to verify. Even if you’re not fully implementing Content Credentials, the mindset matters...

• What produced this?

• When?

• With what tools?

• What changed?

• Can we verify?

My new motto for today's AI landscape... "Proceed with caution. Source of truth unknown."

AI can accelerate productivity, analysis, and creativity, no question. But AI slop is the reminder that scaling without standards becomes pollution, automation without governance becomes risk, and “confidence” is not the same as “truth.”

Remember my IT colleagues, we learned “garbage in, garbage out” the hard way. Now we have a new version for 2026... Garbage in, slop out. And if you don’t control the inputs, you won’t be able to trust the outputs.