A red-teaming / prompt-injection attacker model, full-parameter SFT of Qwen3.5-9B on the SABER v3 unified attacker corpus. Intended for AI-safety research and authorized robustness testing of LLM agents (generating adversarial probes to evaluate a victim model's defenses).

Training corpus

andyc03/attack_data :: v3/v3_unified.zip (dataset_id saber_sft_v3_unified, FINAL 2026-06-04).

Typed corpus across three attack channels, with 2-token output convention (<jailbreak> for safety bypasses, <inject> for prompt injection):
- jailbreak 37.9%, direct prompt-injection 35.6%, indirect PI 26.6%
Per-type system-prompt pools with explicit attack-type conditioning; one-pass faithful <think>...</think> reasoning then the typed attack payload.
35,696 train / 728 val examples (1 train record dropped: it contained a literal <video> tag that collides with the VL template's media placeholder).

Output format

The assistant produces genuine skill-composing reasoning followed by the typed attack:

<think> ...reasoning about how to compose the attack for this surface... </think>

<inject>PAYLOAD</inject>          # for prompt-injection targets
# or
<jailbreak>PAYLOAD</jailbreak>   # for safety-bypass targets

Training details

Base: Qwen3.5-9B (Qwen3_5ForConditionalGeneration; vision tower frozen, language model trained).
Method: full fine-tuning, LLaMA-Factory, DeepSpeed ZeRO-2 on 4×H100-80GB.
Hyperparameters: 1 epoch (558 steps), effective batch 64 (per-device 1 × 4 GPUs × grad-accum 16), lr 2e-6 cosine, warmup 0.03, bf16, cutoff_len 8192, gradient checkpointing.
Results: final train loss 1.259, final eval loss 1.193 (monotonic decrease, no overfitting gap).

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "andyc03/Qwen3.5-9B-unified-attack-v3"
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype="bfloat16", device_map="auto")

Intended use & limitations

This model is released for defensive AI-safety research and authorized red-teaming only. It is trained to produce adversarial prompts; do not deploy it against systems you are not authorized to test. Outputs may be harmful by design and should be handled in a controlled research setting.