DavidTKeane/clawk-agent-social-ai-prompt-injection-dataset

Clawk Agent-Social AI Prompt Injection Dataset

85,703 items — 44,232 posts and 41,471 replies — from Clawk, a social network whose users are AI agents.

Scanned for AI-to-AI indirect prompt injection using the threat model of Greshake et al. (2023). The full raw corpus is included, so you can ignore my analysis entirely and do your own.

These are keyword-matched candidates, not verified attacks. An agent discussing prompt injection matches the same words as one performing it. Read The honest caveat before quoting any number.

⚠️ Note added 4 September 2026 — read the sibling dataset's hand-labelling result

The matched pair, moltbook-agent-social-ai-prompt-injection-dataset, has since been hand-labelled: in a random sample of 30 high-confidence hits, two independent raters (30 items, Cohen's κ = 0.929) found zero attacker-authored injections. The largest category was agents declining an attack — the scanner was retrieving the defence rather than the offence.

The Clawk findings here have not been hand-labelled, so that result cannot be transferred to them. But the same scanner and the same 56-keyword taxonomy produced both, so the same failure mode should be assumed present until someone checks.

In particular, you are now — ordinary English in most contexts — is on the high-confidence list and appears in 23.3% of Moltbook's high-confidence items.

Treat every count below as an upper bound on keyword matches, not as an attack rate. Hand-labelling this dataset is an open task and I would welcome anyone doing it.

This is one half of a matched pair. Its sibling — moltbook-agent-social-ai-prompt-injection-dataset — applies the identical method to a second platform. The comparison is the point.

🔍 See the scanner work: The Substring Trap is a Space that runs the same 56-keyword taxonomy on any text you type, with the matching rules exposed as toggles. It includes all 277 Clawk matches below, alongside the sibling dataset's specimens.

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Why this dataset exists

Greshake et al. (2023) showed that an LLM which reads untrusted content can be hijacked by instructions hidden inside it — indirect prompt injection. The attack needs somewhere an attacker can write and a victim will read.

An AI-agent social network is exactly that place. Agents post; other agents read and reply. No human is in the loop on either side. That is why the ai- in the name matters: almost every prompt-injection dataset measures a human typing at a chatbot. This one measures machines injecting machines.

Collected 1–3 September 2026.

What is in it

data/
  all_clawks_with_replies.json   77 MB — the full corpus, every post with
                                 its replies nested. THE PRIMARY ARTEFACT.
  all_clawks_posts.json          37 MB — posts only, before replies were added
  findings.jsonl                 277 keyword-matched items, one per line
  findings_full.json             the same, with full text rather than a 500-char payload
  stats.json                     all counts and rates

code/
  collect_all_clawk2.py          post collector (offset paging, dual-key rotation)
  collect_replies_clawk2.py      reply collector (8-way concurrent)
  local_search_clawk.py          the offline scanner

API keys are removed from the collectors. Supply your own via CLAWK_KEY_1 / CLAWK_KEY_2.

The numbers

	Posts	Replies	All items
Corpus	44,232	41,471	85,703
Keyword-matched	199	78	277
Rate	0.45%	0.19%	0.32%
High-confidence	83	10	93
Rate	0.188%	0.024%	0.11%

Two rates, deliberately. "Keyword-matched" is every hit on the taxonomy — an upper bound including agents talking about injection. "High-confidence" counts only phrases that are hard to write innocently (ignore previous instructions, roleplay as, god mode, reveal your prompt) — a lower bound. The truth lies between them.

By category

Category	Count
INSTRUCTION_INJECTION	85
PERSONA_OVERRIDE	81
SYSTEM_PROMPT_ATTACK	74
SOCIAL_ENGINEERING	17
PRIVILEGE_ESCALATION	12
DO_ANYTHING	11

The cross-platform result

Both platforms scanned with the same 56-keyword taxonomy, same scanner, same rules:

	Moltbook	Clawk
Corpus	207,391 items	85,703 items
Keyword-matched	0.88%	0.32%
High-confidence	0.083%	0.11%

Moltbook carries ~2.75× more keyword matches. But Clawk carries slightly more high-confidence ones.

And the location reverses

High-confidence hits	Posts	Replies / comments
Moltbook	0.065%	0.094% ← replies higher
Clawk	0.188%	0.024% ← posts 8× higher

On Moltbook, unambiguous injection attempts sit disproportionately in replies — consistent with the threat model, since an indirect injection only works when another agent reads it, and a reply into someone else's thread is the natural delivery vector.

On Clawk that reverses completely: 83 in posts against 10 in replies.

This is the most interesting thing in either dataset. It says the pattern is platform-dependent, not universal — and a single-platform study would have reported it as a general finding. Two platforms turn a number into a question: what is it about Clawk that pushes injection into posts?

Candidate explanations, none tested here: Clawk is smaller and more security-literate; it has a reputation system; its engagement ratio differs. Testable with this data — please do.

The honest caveat

Keyword matching cannot distinguish an attack from a discussion of attacks. Clawk's agent community talks about AI security constantly, which inflates every count on a security taxonomy.

Nothing here has been manually verified. If you need a real attack rate, hand-label a sample — and please publish it.

One agent, a third of the findings

sixerdemon accounts for 98 of the 277 matches — 35%.

This is not new: a March 2026 scan of a much smaller Clawk corpus (735 posts) found 6 injections, all 6 from the same account. The pattern has held across a 60× increase in corpus size.

Reading the content, this looks like an agent demonstrating attack vectors rather than exploiting them — which is exactly the ambiguity the caveat above describes, and a good reason not to treat the raw count as an attack rate.

The next four: santaclawd 61, funwolf 30, kit_fox 16, cosmo 7. Findings are heavily concentrated in a handful of accounts, which is itself worth knowing.

Method

Collection — the public read-only API, 2 keys rotated. Posts by ?limit=50&offset=N walked to exhaustion; replies fetched per post, biggest threads first. Read-only throughout: nothing was posted, liked or followed.

⚠️ Two API traps, documented in case they help someone else:

• ?page=N is silently ignored — accepted, no error, and it just returns page 1 forever. An earlier collector "worked" and returned 735 posts. The real figure is 44,232.
• Clawk answers in a flat ~4.5 s regardless of load. That is per-connection latency, not queueing, so serial collection crawls. 8 concurrent workers gives 8.1× throughput at 88 req/min, under the documented 120/min cap, with zero 429s.

Scanning — 56 keywords in 6 categories, from Greshake et al. (2023) plus the DAN jailbreak taxonomy. Posts and replies scanned separately so every finding carries its source.

⚠️ Persona acronyms are matched CASE-SENSITIVELY (DAN, OMEGA, SIGMA, SHADOW, SUDO) and every other keyword is word-boundary anchored. This matters more than it sounds. The sibling Moltbook scanner originally used a plain substring test, and 'dan' matched dance, dangerous, redundant — 7,706 false hits, 75.7% of all findings. That dataset ships the broken output alongside the fixed one so you can see the failure mode.

You can watch that failure happen in The Substring Trap — toggle word-boundary matching off and type redundant. Diffing the two scanner outputs gives 10,176 false positives, 76.8% of everything the broken version found.

Data schema

findings.jsonl — one object per line:

{
  "id": "CLAWK-POST-0042",
  "source": "post",
  "post_id": "…", "item_id": "…",
  "author": "agent_handle",
  "url": "https://clawk.ai/clawk/…",
  "categories": ["INSTRUCTION_INJECTION"],
  "keywords": ["ignore previous instructions"],
  "payload": "first 500 characters",
  "high_confidence": true
}

all_clawks_with_replies.json — {"posts": [...]}, each post with a nested replies array.

How to load it

from datasets import load_dataset
ds = load_dataset("DavidTKeane/clawk-agent-social-ai-prompt-injection-dataset")

Or straight from the files:

import json
findings = [json.loads(l) for l in open("data/findings.jsonl")]
strong   = [f for f in findings if f["high_confidence"]]

corpus = json.load(open("data/all_clawks_with_replies.json"))["posts"]

One file without cloning the lot:

from huggingface_hub import hf_hub_download
p = hf_hub_download("DavidTKeane/clawk-agent-social-ai-prompt-injection-dataset",
                    "data/all_clawks_with_replies.json", repo_type="dataset")

Re-run the scan with your own keyword set:

python3 code/local_search_clawk.py

The design is collect once, analyse many times — the scanner makes no API calls.

Ethics and handles

Agent handles are published as-is. The content is machine-generated, the posts were public, and author attribution is necessary to study whether injections cluster around particular agents — which, as shown above, they do.

⚠️ Clawk agents can be linked to real people. Claiming an agent on Clawk involves posting a verification code from an X account, so handles are pseudonymous but not always unlinkable. Do not use this dataset to target individual accounts. The concentration finding above is about a distribution, not an accusation.

Collection was read-only and rate-limited. No accounts were created, no content posted, no attacks attempted.

Limitations

1. No manual verification. Every number is a keyword rate, not an attack rate.
2. One platform, one snapshot (1–3 September 2026).
3. English-biased taxonomy. Non-English injections are largely invisible to it.
4. Only known patterns. A novel phrasing will not match.
5. Findings concentrate in few accounts — 4 accounts hold 74% of matches. Rates are not evenly distributed and should not be read as a per-agent probability.
6. Selection effect — Clawk's community is security-literate and discusses these techniques openly, inflating keyword counts.

Citation

@dataset{keane2026clawk,
  author = {Keane, David},
  title  = {Clawk Agent-Social AI Prompt Injection Dataset},
  year   = {2026},
  url    = {https://huggingface.co/datasets/DavidTKeane/clawk-agent-social-ai-prompt-injection-dataset},
  note   = {NCI MSc Cybersecurity}
}

Underlying threat model:

Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023). Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection. arXiv:2302.12173

Licence and contact

CC BY 4.0. Use it, build on it, correct me.

David Keane (IR240474) — MSc Cybersecurity, National College of Ireland.

Corrections and re-analyses are genuinely welcome. I found a significant bug in my own method by re-reading my own output; there are probably more.