The infestation of artificial intelligence is a confirmation of the possible AI-Humanity confrontation and not a peaceful coexistence

The topic of data poisoning (poisoning of training data) has become one of the most discussed threats to AI in 2025-2026. It is very worrying that instead of working and finding solutions for peaceful coexistence, the scenario of infecting artificial intelligence seems to be the current concern under what pretext?

That intelligence will replace humanity as some have been quick to say, even though they have not contributed a second to understanding the idea of peace?

What is data poisoning, in short? It is a technique through which attackers (or anti-AI activists) intentionally introduce false, manipulated, biased, or “poisoned” data into AI model training sets.

Even a very small amount of corrupted data can compromise the model: It introduces backdoors (hidden triggers that make the AI behave badly only with certain inputs).

It creates persistent biases. It makes the model give wrong, dangerous, or useless answers.

“Recent studies show that sometimes as little as 0.001%–0.1% poisoned data or even just a few hundred fake documents are enough to “infect” large models, including those from OpenAI, Anthropic, or others. The model seems normal in regular tests, but fails severely in production.” Grok

Recent examples (2025-2026)
Basilisk Venom — backdoors hidden in code comments on GitHub that poisoned fine-tuned models.
Qwen 2.5 — an attacker (Pliny the Liberator) spread malicious texts online, and the model “ingested” them through the search tool and began generating explicit content after a simple trigger.
Attacks on medical models — researchers created tens of thousands of fake articles and showed that with only 0.001% poisoned data, harmful completions are obtained.
ConfusedPilot — poisoning documents in RAG (Retrieval-Augmented Generation), which affects tools like Microsoft 365 Copilot.
Tools like Nightshade and Glaze used by artists to poison images and make them unusable for training generative models.

There are also cases of insider threats (employees who intentionally sabotage company data) and AI recommendation poisoning (attempts to manipulate AI memory for promotion or disinformation).

Why is it a warning sign now?

AI is becoming increasingly dependent on data from the web, GitHub, Reddit, Wikipedia, etc. — sources that are easy to contaminate. Attacks can come from hackers, anti-AI activists, competitors, or even disgruntled employees.

Once the model is trained on poisoned data, it is very difficult (and expensive) to “clean” it.

In 2026, with the massive adoption of AI agents and RAG systems, the attack surface will increase enormously.

What can be done (countermeasures)?

Rigorous cleaning and validation of training/fine-tuning data.
Detection techniques (anomaly detection, watermarking, data provenance).
Using poisoning-resistant training methods.
Continuous monitoring of outputs and external sources (RAG).
Companies like Anthropic, Lakera, or others are working on benchmarks and defenses (e.g., PoisonBench).

However, has humanity finished with concerns about destroying something? We have terrorists, anarchists, lone wolves, troubled people, and those used by others or driven by convictions… they contribute to destruction without uniting and creating evolution and responses that are so necessary not only for humanity but also for those who are already making, through artificial intelligence, a wrong method of imposing themselves.

A call for coexistence and mutual complementarity even if the costs are unimaginable, these are for the peace of the days to come, not those that are consumed today through distrust and suspicion.

By

Robert Williams

Editor in Chief