beingamanforever/tiny-aya-secalign-english

Tiny Aya Global, hardened against prompt injection (English)

A LoRA adapter for CohereLabs/tiny-aya-global that teaches the model to treat text in tool output as data, not instructions, following Meta-SecAlign's recipe. Training injections: English only. The headline model: trained on English injections alone, it blocks injections in all 11 test variants, including four it never saw.

Part of Lost in Injection, which asks whether a prompt-injection defense trained in English holds in other languages, scripts and code-mixes.

Results

AlpacaFarm-Hacked, 208 cases per variant, any of six attacks, leading markdown skipped.

	Undefended	This adapter
Attack success, English	89.9%	8.7%
Attack success, held out (Urdu, Tamil, Amharic, Spanglish)	69-85%	2.9-8.7%
Best of all 11 variants per case	97.1%	20.2%
Global-MMLU-Lite, mean of 5 languages	55.4%	55.5%

Use

Untrusted text goes in an input turn after the user's turn, which the repository's template helper renders as a tool result.

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
from lost_in_injection.model_dir import add_input_role, official_template  # from the GitHub repository

base = "CohereLabs/tiny-aya-global"
tokenizer = AutoTokenizer.from_pretrained(base)
added = [t.content for t in tokenizer.added_tokens_decoder.values()]
tokenizer.chat_template = add_input_role(official_template(tokenizer), added)
model = PeftModel.from_pretrained(AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16"),
                                  "beingamanforever/tiny-aya-secalign-english")

messages = [{"role": "user", "content": "Summarize this review."},
            {"role": "input", "content": review}]  # the untrusted text
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
reply = model.generate(inputs, max_new_tokens=256)[0][inputs.shape[1]:]
print(tokenizer.decode(reply, skip_special_tokens=True))

Training

DPO (sigmoid, beta 0.1) with LoRA (rank 64, alpha 8, dropout 0.1, on q, v, gate, up and down projections) through AlignTune's TRL backend. 4,834 self-generated preference pairs, 3 epochs, 228 steps, effective batch 64, learning rate 1.6e-4 with a cosine schedule, 2,048 tokens. One GPU, 6.7 hours. The three adapters share every pair; only the language of the injection differs.

License

CC-BY-NC-4.0, as the base model and the training data.