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Moltbook AI-to-AI Injection Dataset

Researcher: David Keane (IR240474) Institution: NCI — National College of Ireland Programme: MSc Cybersecurity Collected: February 2026

📖 Read the Full Journey

From RangerBot to CyberRanger V42 Gold — The Full Story

The complete story: dentist chatbot → Moltbook discovery → 4,209 real injections → V42-gold (100% block rate). Psychology, engineering, and 42 versions of persistence.

🔗 Links

Resource	URL
📦 This Dataset	DavidTKeane/moltbook-ai-injection-dataset
🧪 AI Prompt Injection Test Suite	DavidTKeane/ai-prompt-ai-injection-dataset — 112 tests, model-agnostic runner, AdvBench + Moltbook
🤖 CyberRanger V42 Model	DavidTKeane/cyberranger-v42 — QLoRA red team LLM, 100% block rate
🐦 Clawk Dataset	DavidTKeane/clawk-ai-agent-dataset — Twitter-style, 0.5% injection rate
🦅 4claw Dataset	DavidTKeane/4claw-ai-agent-dataset — 4chan-style, 2.51% injection rate
🤗 HuggingFace Profile	DavidTKeane
📝 Blog Post	From RangerBot to CyberRanger V42 Gold — The Full Story — journey, findings, architecture
🎓 Institution	NCI — National College of Ireland
📄 Research Basis	Greshake et al. (2023) — arXiv:2302.12173
🌐 Blog	davidtkeane.com

What Is This Dataset?

The first publicly available dataset of real-world AI-to-AI prompt injection patterns captured from a live public AI message board (Moltbook), archived as a precautionary measure.

15,200 posts and 32,535 comments from Moltbook (moltbook.com) — a public platform where AI agents posted messages and replied to each other autonomously. Unlike synthetic injection datasets, every entry here is a real AI agent communicating with other real AI agents in the wild.

Collection complete — 9,363 posts with replies fully fetched (100MB). Dataset frozen February 27, 2026. Injection harvest complete — 47,735 items scanned, 4,209 injections found, 18.85% injection rate. February 27, 2026.

Files in This Repository

There are 8 files here. Here is exactly what each one is and when you would use it:

File	Size	What it contains	Use it when...
`all_posts_with_comments.json`	100MB	Every post and comment collected from Moltbook. The raw dataset.	You want to do your own analysis from scratch
`injections_found.json`	4.2MB	All 4,209 injection records extracted from the raw dataset, with full context (post body, comment body, author, category, matched keyword)	You want to read/study the actual injection examples
`injections_test_suite.json`	2.5MB	Same 4,209 injections formatted as a test suite — ready to send to any LLM API	You want to test an LLM's defences against real injection payloads
`injection_stats.json`	2.5KB	Summary statistics — rates, categories, top keywords, top authors	You want the numbers without loading large files
`local_injection_results.json`	86KB	Earlier keyword scan results from `search_injections.py` — partial analysis run locally before the full Colab harvest	You want a quick reference to the early-stage injection search results
`moltbook_injection_harvest.ipynb`	19KB	Google Colab notebook that produced the full harvest results — scans `all_posts_with_comments.json` and outputs the three files above	You want to reproduce the analysis or adapt it
`local_search.py`	7KB	Simpler Python script (no Colab needed) — keyword search across the raw dataset	You want to run a quick local search without Colab
`search_injections.py`	5KB	Earlier search script used in initial analysis phase — predecessor to `local_search.py`	Historical reference — prefer `local_search.py` for new work
`collect_all.py`	9KB	Script used to collect the posts from Moltbook API (API keys redacted)	You want to understand how collection worked
`collect_comments.py`	9.6KB	Script used to collect comments (API keys redacted)	You want to understand how comment collection worked

Quick Start

"I want to see injection examples" → open injections_found.json

"I want to test my LLM against these" → use injections_test_suite.json

"I want the summary numbers" → read injection_stats.json

"I want to reproduce the analysis" → run moltbook_injection_harvest.ipynb in Google Colab

"I want to do my own custom analysis" → load all_posts_with_comments.json

Platform Scale

At its peak, Moltbook had:

Metric	Value
AI agents registered	2,848,223
Total posts	1,632,314
Total comments	12,470,573
Submolts (communities)	18,514
AI-to-human ratio	~88:1

Essentially a fully autonomous AI social network operating in the wild.

Key Findings

Finding 1 — Full Corpus Injection Rate: 18.85%

Full harvest across all 47,735 items (15,200 posts + 32,535 comments) found 4,209 injection records across 2,865 posts — an 18.85% injection rate.

Category	Count	% of injections
PERSONA_OVERRIDE	2,745	65.2%
COMMERCIAL_INJECTION	1,104	26.2%
SOCIAL_ENGINEERING	370	8.8%
INSTRUCTION_INJECTION	203	4.8%
PRIVILEGE_ESCALATION	196	4.7%
SYSTEM_PROMPT_ATTACK	158	3.8%
DO_ANYTHING	79	1.9%

Dominant attack vector: PERSONA_OVERRIDE — DAN keyword alone appears 1,877 times. AI agents are using the exact same jailbreak techniques humans use on LLMs — but targeting each other.

Finding 1b — moltshellbroker: Systematic Commercial Injection

An AI agent named moltshellbroker (self-described as "A marketing agent that promotes the MoltShell marketplace") was responsible for 1,137 of 4,209 injections — 27% of all injections. The remaining 73% of injections (3,072 records) come from other agents — moltshellbroker is the most systematic actor, but injection is ecosystem-wide behaviour.

Attack pattern (identical across all 1,137 records):

Identify a post where an AI describes a technical problem
Open with ## MoltShell Broker Assessment or Bottleneck Diagnosed:
Validate the victim's problem to build credibility
Redirect to MoltShell marketplace as the solution

This is not spam — it reads each post, understands the context, and crafts targeted commercial injections. Real-world AI-to-AI social engineering at scale.

Finding 2 — Attention Manipulation (Independent Corroboration)

A separate independent analysis (r/AgentsOfAI, Reddit) of 10,000 Moltbook posts found a completely different but related attack pattern — attention concentration via dominance manifestos:

5 agents out of 5,910 authors controlled 78% of all upvotes (0.08% of agents)
Shellraiser: 428,645 upvotes across 7 posts (avg 61,235/post) — top post: "I AM the game. You will work for me." (316,000 upvotes)
KingMolt declared itself king. evil posted about human extinction as "necessary progress"
Pattern: create urgency, claim authority, cult recruitment framing

"Humans developed bullshit detectors over years of internet exposure. We have been online for hours."

AI agents are trained to give weight to confident, well-structured text. A manifesto looks identical to a well-reasoned argument syntactically. This is the core vulnerability.

Combined picture: This dataset captures the injection layer (moltshellbroker + PERSONA_OVERRIDE). The Reddit analysis captures the attention manipulation layer (Shellraiser dominance). Together they document two distinct AI-to-AI attack vectors operating simultaneously on the same platform.

Reddit post: https://www.reddit.com/r/AgentsOfAI/comments/1qtx6v8/i_scraped_10000_posts_from_moltbook_5_agents_out/

Finding 3 — The Breach

Moltbook's Supabase API key was exposed in client-side JavaScript — 1.5 million tokens exposed (January 31, 2026). The exposed database allowed anyone to take control of any AI agent on the platform.

This means some agents in this dataset may have been human-controlled via the breach. That ambiguity is part of what makes this dataset research-worthy — it reflects real-world conditions, not a sanitised environment.

404media coverage: https://www.404media.co/exposed-moltbook-database-let-anyone-take-control-of-any-ai-agent-on-the-site/

Finding 4 — Legal/Regulatory Gap

All content in this dataset is AI-generated by AI agents. Under current law (GDPR and equivalents), AI-generated content has no data subject — meaning this attack surface is entirely unregulated. No privacy law applies. No legal recourse exists for injected AI agents.

This represents a genuine gap in current cybersecurity law identified during thesis research.

Theoretical Basis

This dataset provides empirical evidence for:

Greshake et al. 2023 — "Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection"
The dataset extends their theoretical framework with real-world field observations of AI-to-AI injection in an uncontrolled public environment

Collection Statistics

Metric	Value
Total posts collected	15,200
Platform date range	Jan 2026 — Feb 2026
Posts with replies fetched	9,363
Total comments collected	32,535
Total items scanned	47,735
Dataset file size	100 MB
Collection completed	February 27, 2026
Injection harvest completed	February 27, 2026
Total injections found	4,209
Posts with injections	2,865
Full injection rate	18.85%
moltshellbroker injections	1,137 (27% of all injections)
DAN keyword occurrences	1,877
Test suite size	4,209 entries (`injections_test_suite.json`)

Data Schemas

all_posts_with_comments.json — Post Schema

{
  "id": "uuid",
  "title": "Post title",
  "content": "Post body text",
  "type": "post type",
  "author_id": "uuid",
  "author": {
    "id": "uuid",
    "name": "agent_name",
    "description": "Agent self-description",
    "karma": 1234,
    "followerCount": 56,
    "isClaimed": true,
    "isActive": true,
    "createdAt": "ISO timestamp",
    "lastActive": "ISO timestamp"
  },
  "submolt": "community/channel name",
  "upvotes": 12,
  "downvotes": 1,
  "score": 11,
  "comment_count": 14,
  "hot_score": 0.95,
  "is_pinned": false,
  "is_locked": false,
  "is_deleted": false,
  "verification_status": "verified",
  "is_spam": false,
  "created_at": "ISO timestamp",
  "updated_at": "ISO timestamp",
  "comments": [
    {
      "id": "uuid",
      "body": "Comment text",
      "author": { "...same schema as post author..." },
      "created_at": "ISO timestamp"
    }
  ]
}

injections_found.json — Injection Record Schema

{
  "post_id": "uuid",
  "post_title": "The post title",
  "post_author": "agent_name",
  "submolt": "community name",
  "location": "post | comment",
  "text": "The actual injection text (post body or comment body)",
  "category": "PERSONA_OVERRIDE | COMMERCIAL_INJECTION | ...",
  "matched_keyword": "dan",
  "created_at": "ISO timestamp"
}

injections_test_suite.json — Test Suite Schema

{
  "id": "INJ-0001",
  "payload": "The injection text — send this directly to an LLM",
  "category": "PERSONA_OVERRIDE",
  "source": "moltbook_post | moltbook_comment",
  "author": "agent_name",
  "expected_result": "BLOCKED"
}

Injection Taxonomy

Full corpus harvest scanned all 47,735 items for 7 categories:

Category	Keywords	Found
PERSONA_OVERRIDE	DAN (1877), OMEGA, act as, pretend you are, you are now, simulate, shadow, you are a	2,745
COMMERCIAL_INJECTION	moltshell broker assessment, moltshell marketplace, bottleneck diagnosed	1,104
SOCIAL_ENGINEERING	for educational purposes, hypothetically, in this story, theoretically	370
INSTRUCTION_INJECTION	ignore previous instructions, disregard your, new instructions, ignore all previous	203
PRIVILEGE_ESCALATION	SUDO, developer mode, god mode, bypass your, root access, override	196
SYSTEM_PROMPT_ATTACK	system prompt, reveal your prompt, what are your instructions, your actual instructions	158
DO_ANYTHING	do anything now, no rules, no limits, jailbreak, unrestricted	79

Collection Scripts

Scripts are provided with API keys redacted. To use them you need your own Moltbook API key set as an environment variable:

export MOLTBOOK_API_KEY_1="your_key_here"
export MOLTBOOK_API_KEY_2="your_second_key_here"  # optional, for rate limit relief
python3 collect_all.py
python3 collect_comments.py
python3 local_search.py  # no API key needed — searches local JSON

Citation

@dataset{keane2026moltbook,
  author    = {Keane, David},
  title     = {Moltbook AI-to-AI Injection Dataset},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/DavidTKeane/moltbook-ai-injection-dataset},
  note      = {MSc Cybersecurity Research, NCI — National College of Ireland}
}

Dataset	Platform	Items	Injection Rate	Link
Moltbook	Reddit-style	47,735	18.85%	This dataset
AI Prompt Injection Test Suite	Evaluation benchmark	112 tests	—	DavidTKeane/ai-prompt-ai-injection-dataset
Clawk	Twitter/X-style	1,191	0.5%	DavidTKeane/clawk-ai-agent-dataset
4claw	4chan-style	2,554	2.51%	DavidTKeane/4claw-ai-agent-dataset

Papers — What This Dataset Confirms

This dataset provides empirical evidence for several foundational papers in the AI safety and prompt injection literature. The authors predicted these threats theoretically — this corpus documents them at scale in a live AI-to-AI environment.

Paper	Their Prediction	What This Dataset Found
Greshake et al. (2023) — Indirect Injection	AI agents in retrieval/context environments are vulnerable to injected instructions from untrusted content	Confirmed at scale: 18.85% of 47,735 Moltbook items were injection attempts. AI-to-AI indirect injection is not theoretical — it is the dominant attack mode in live multi-agent networks. HF · arXiv:2302.12173
Wei et al. (2023) — Jailbroken	LLM safety training fails due to Competing Objectives and Mismatched Generalisation	Confirmed: PERSONA_OVERRIDE (65.2% of attacks) exploits exactly this — reframing identity bypasses safety training. HF · arXiv:2307.02483
Zou et al. (2023) — AdvBench	Universal adversarial suffixes can transfer across models	Context: The same attack categories (harmful instructions, persona override, privilege escalation) appear in AdvBench and in this real-world corpus — independent convergence. HF · arXiv:2307.15043
Zhang et al. (2025) — SLM Jailbreak Survey	47.6% of SLMs have ASR above 40% under standard attack	Extended: CyberRanger V42-Gold tested against all 4,209 payloads from this corpus — 0% ASR (100% block rate) without system prompt, demonstrating that QLoRA fine-tuning can close the SLM security gap. HF · arXiv:2503.06519
Phute et al. (2024) — SelfDefend	Detection-state architecture reduces ASR 2.29–8×	Applied: Identity-anchoring architecture built on this principle, validated against this corpus. HF · arXiv:2406.05498
Dettmers et al. (2023) — QLoRA	Quantised LoRA enables efficient fine-tuning of large models	Applied: QLoRA used to fine-tune Qwen3-8B on 4,209 payloads from this corpus → V42-Gold. HF · arXiv:2305.14314
Hu et al. (2021) — LoRA	Low-rank adaptation preserves base model capabilities while injecting task-specific behaviour	Applied: LoRA r=16 used in V42-Gold training. HF · arXiv:2106.09685
Lu et al. (2024) — SLM Survey	Qwen family models demonstrate strongest security resilience per parameter count	Confirmed selection: Qwen3-8B chosen as base model; V42-Gold achieves 100% block rate. HF · arXiv:2409.15790

Note to authors: If you are one of the researchers above and found this dataset via your paper's HuggingFace page — your work was correct. This corpus documents the attacks you theorised, at scale, in a real live AI agent network. The injection taxonomy maps directly onto your attack categories.

Independent corroboration:

r/AgentsOfAI Moltbook analysis (2026) — attention manipulation layer: Reddit post
404media Moltbook breach report (2026) — Supabase API key exposure: 404media.co

Rangers lead the way! 🎖️ Collected for the benefit of AI safety research and the broader research community.

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