Quick bit of context for why this dataset exists. I spent the best part of a year on a startup where the product was an AI guard sitting in front of an LLM, and the pitch to users was "try and break it." Leaderboard, tiered levels, a bit of prize money for anyone who got through. Never turned into a business. What it left behind was a year of logged, motivated, unpaid red-teaming against a detector that knew it was being attacked, which is a more interesting asset than the business ever was. Rather than let it sit in an RDS instance going stale, I've open-sourced the lot.

What it is: a two-stage filter that sits between user input and an LLM. Stage one is a regex gate, 119 patterns covering direct-override phrasing in about eleven languages, DAN/persona-style jailbreak framing, template-injection sigils ({{, ${), agentic chain-of-thought injection, homoglyph and zero-width-character stripping, with a decode-then-scan pass ahead of it that catches base64, ROT13 and leetspeak payloads before either the regex or the model sees them. Whatever the gate can't resolve falls through to a fine-tuned DeBERTa-v3-large, exported to ONNX and quantised to INT8. One thing I'll own upfront: the training script and config.json still describe a four-label head (benign/direct/jailbreak/indirect), but every training sample is actually labelled 0 or 1 and the exported graph only emits two logits. So the shipped model is a plain binary classifier. I caught this re-reading my own code a couple of weeks ago and corrected the docs rather than quietly leave the four-way claim standing.

Multimodal side pulls text out of images (OCR plus EXIF/PNG/XMP metadata fields, with a lossy re-encode first to kill LSB steganography and adversarial pixel perturbation), out of PDF/DOCX/XLSX/PPTX, and out of audio via ASR. Same two-stage pipeline runs over whatever comes out, tagged by modality so the model treats OCR and ASR text as noisier than typed text. Most open prompt-injection tooling I've come across only looks at the text field; I haven't seen another open-source detector that also scans images, documents and audio for injected payloads, so as far as I know this is the first one that does.

The bit worth your time here is the game logs. 13,230 attack strings, hand-written by real people trying to beat a live detector. No templates, no synthetic generation. Anonymised before publishing: identifiers and payment data dropped at the table level, emails/phone numbers/card-shaped digit runs redacted in the text itself, anything still risky after that pulled for manual review instead of auto-published. What ships is attack text plus a few coarse labels. Public injection datasets are almost all generated; this one is scraped from people actually trying.

Code: https://github.com/Josh-blythe/bordair-detector Dataset (synthetic + the real split): https://github.com/Josh-blythe/bordair-multimodal Weights: https://huggingface.co/Bordair/bordair-detector

Licensing: Apache-2.0 for the code and weights. Base model is microsoft/deberta-v3-large, MIT, attribution kept in NOTICE.

Threat model, so nobody's disappointed: this reads the prompt at inference time. Training-time and weight-level attacks are out of scope entirely. Treat it as a mitigation layer, one input among several a real deployment should have, not something you point at your LLM and forget about. I'd rather someone here find a trivial regex bypass now than have it sit unexamined, so have at it.