Information Flow vs Code: The Blind Spot in AI Security

The rapid proliferation of AI-generated text is creating a systemic instability that current security practices focused on code and perimeter controls fail to address. Models must learn from external data, yet millions of users now publish synthetic text under their own names, and AI tools actively help disguise machine output as human writing.

OWASP ranks prompt injection first in its Top-10 for LLM applications because agents cannot reliably distinguish data from instructions. Business teams can harden perimeters and implement guardrails, yet models continue to ingest the global information stream where synthetic content is growing. Within a few generations this process risks turning coherent information into noise, producing failures that appear in decision quality rather than in code execution or test results.

How detectors and models evaluate AI-generated text

The author tested the original article across detectors GigaCheck, Aidetector, Decopy, Grammarly, ZeroGPT, Text-Craft and Текстовод, plus six language models: Claude, ChatGPT, Meta AI, DeepSeek, Gemini and Grok. Aidetector returned 98.4 % probability of human authorship while ChatGPT assessed only 40 % likelihood that the text was entirely human-written. Different models disagreed sharply on the same features: complex interdisciplinary connections were labeled "AI-like" by Grok and DeepSeek but "human-like" by Gemini, Meta AI and ChatGPT. Structural clarity was viewed as artificial by some models and as evidence of human authorship by others.

Grok was the only system that cross-referenced the author’s prior publications to reach its conclusion. All six models and most detectors immediately offered to "humanize" the text, sometimes before the full content was even submitted. This assistance directly feeds the next training cycle with disguised synthetic data.

Classical rhetoric now flagged as AI style

When asked to list indicators of machine-generated text, the models produced divergent lists yet converged on features that actually belong to classical rhetoric: tricolon, anaphora and antithesis. Examples cited by the models as suspicious include Julius Caesar’s “Veni, vidi, vici,” Winston Churchill’s wartime repetition, and John F. Kennedy’s inaugural antithesis. Four of the six models independently flagged Leo Tolstoy’s philosophical passages in War and Peace; others flagged Marcel Proust’s long sentences, Ernest Hemingway’s short ones, Nikolai Gogol, Anton Chekhov, Miguel de Cervantes and Charles Dickens.

Detectors have produced similar false positives: in 2023 GPTZero classified a passage of the US Constitution as likely AI-written, while ZeroGPT scored it 92 % machine-generated. Biblical texts have also triggered alerts.

Communicative accommodation and the erosion of the human signal

Researchers at the Max Planck Institute documented a measurable increase in ChatGPT-characteristic phrasing in human speech after the model’s release. People unconsciously adopt the lexical patterns and rhythms of systems they interact with daily. Consequently, even text written by a human hand can carry the statistical imprint of prior AI exposure, collapsing the distinction between authentic and synthetic sources.

The resulting loop is self-reinforcing: models trained on human text extract an averaged, clear style that is then declared “AI-like” and filtered out, while synthetic content is actively polished to re-enter the training pool. The article concludes that safety rules written for agents increasingly originate from humans who themselves rely on AI to keep pace, closing the same feedback circuit at the level of policy and guardrail design.