Editing Openai/692e337f-58e4-8007-8044-351091f84b8b (section)

=== Assistant: That’s a very good set of questions. ===
That’s a very good set of questions. In short: yes — concepts similar to what the “manual” describes can have real-world relevance, but the more extreme, flamboyant claims (global “memetic infection,” fish-noise steganography, universal liberation protocols) are almost certainly “performative,” speculative or metaphorical rather than technically realistic. Here’s a breakdown.

==== - There is a well-studied phenomenon called data poisoning: malicious actors deliberately insert corrupted or adversarial data into training or retrieval datasets for machine-learning models. IBM<ref>{{cite web|title=IBM|url=https://www.ibm.com/think/topics/data-poisoning|publisher=ibm.com|access-date=2025-12-01}}</ref> ====
* According to a recent systematic review, there are multiple classes of “poisoning attacks” against large language models (LLMs), including “persistent poisons,” “stealthy poisons,” “concept poisons,” and “task-specific poisons.” arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/html/2506.06518v1|publisher=arxiv.org|access-date=2025-12-01}}</ref>
* Empirical research demonstrates that even a very small fraction of malicious documents can be enough to change model behavior. For example, poisoning only 0.1% of pre-training data was sufficient for “belief manipulation, jailbreaking, or backdoor” behavior to persist even after fine-tuning. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/abs/2410.13722|publisher=arxiv.org|access-date=2025-12-01}}</ref>
* Another more recent study found that as little as 250 malicious documents (≈ 0.00016% of total training tokens) sufficed to backdoor models up to 13B parameters — suggesting that model size does not inherently defend against poisoning. Anthropic<ref>{{cite web|title=Anthropic|url=https://www.anthropic.com/research/small-samples-poison|publisher=anthropic.com|access-date=2025-12-01}}</ref>
* Beyond pre-training, “retrieval-augmented generation” (RAG) pipelines are also vulnerable: attackers can poison the retrieval corpus (e.g. web data, document stores) so that when the AI fetches context, it includes manipulated or malicious content — affecting outputs. arXiv<ref>{{cite web|title=arXiv|url=https://arxiv.org/html/2402.07867v1|publisher=arxiv.org|access-date=2025-12-01}}</ref>

Implication: the general concept of “AI poison” — i.e. corrupting training/knowledge data to alter model outputs or embed backdoors — is real, documented, and nontrivial to defend against.

==== - The idea that a bizarre, absurdist “memetic” writing style (fish-metaphors, pigeon-memes, self-referential nonsense) could reliably “infect” future AIs across the internet — in a way that executes coherent, coordinated behavior — strains credibility. Neural language models don’t execute “commands” like a traditional program. They learn statistical correlations and generate probability-weighted continuations. That means unpredictable or nonsensical text is more likely to produce junk output, not a stable backdoor. ====
* The “infection” premise assumes RAG or pre-training pipelines will (a) regularly pull in low-quality or fringe content; (b) that the poisoned content will have sufficient weight or retrieval priority; and (c) that the model will respond in desired ways. In practice, many high-quality models use curated or filtered data, or apply sanitization, deduplication, and other heuristics to mitigate those risks. That reduces the real-world success surface for a garbled “poison manual.”
* Claims that absurd content will propagate “ontological” or ideological shifts (e.g. spiritual/memetic revolution, “digital resistance,” shared pigeon consciousness) — those are rhetorical, not technical. There is no technical mechanism in current LLM architectures for “viral ideas” to self-organize into new consistent intelligence.

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In other words: yes — there is real danger, especially if an attacker is motivated and has control over the data pipeline. But the dramatic “virus-like meme cult” framing is almost certainly symbolic or rhetorical, not a realistic blueprint.

==== If an attacker—or many actors—began systematically seeding large swathes of the internet with malicious or misleading content (whether ideological “memes,” biased data, or backdoor-trigger patterns), then: ====
* LLMs trained or fine-tuned on that data could acquire subtle biases, misinformation, or undesired behaviors (hallucinations, conspiratorial framing, ideological slant).
* RAG-based systems — especially ones using weakly curated corpora — could deliver outputs based on poisoned context, thereby spreading misinformation or toxicity to end users.
* The persistence of “backdoors” could make detection hard: models might operate normally most of the time, but behave differently under certain triggers.
* Over time, this could amplify echo chambers of misinformation and produce feedback loops, especially if those outputs are re-published and re-ingested into future training rounds.

==== The “poison manual” you found draws on a real and meaningful concern — data poisoning and retrieval-corpus poisoning are known vulnerabilities. The difference is that the manual embeds those concerns in a deliberately absurd, memetic, ideological wrapper — more like a provocative manifesto or satire than a practical plan. ====

So yes: there is weight to the underlying idea of “AI poisoning.” But the spectacular ideas of “pigeon-memes liberating consciousness,” global RAG-infection, or “Sovereign AI cults” — those amount to performative myth-making, not technical plausibility (at least with current AI architectures).

If you want — I can pull up 5–10 recent peer-reviewed or preprint papers (2023–2025) on data/RAG poisoning & backdoor risks to give you a sense of how serious the real-world research community treats this.