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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,146 @@
+---
+license: mit
+datasets:
+- perplexity-ai/browsesafe-bench
+language:
+- en
+metrics:
+- f1
+- precision
+- recall
+base_model:
+- perplexity-ai/browsesafe
+library_name: transformers
+tags:
+- security
+- prompt-injection
+- ai-safety
+- browser-agents
+- html
+---
+
+# BrowseSafe: Understanding and Preventing Prompt Injection Within User Agent Environment AI Browser Agents
+
+## Highlights
+
+BrowseSafe is a multi-layered defense strategy comprising both architectural and model-based defenses to protect against evolving prompt injection attacks. It is a specialized security model designed to protect AI browser agents from prompt injection attacks embedded in real-world web content.
+
+- **State-of-the-Art Detection**: Achieves a 90.4% F1 score on the BrowseSafe-Bench test set.
+
+- **Real-Time Latency**: Optimized for agent loops, enabling async security checks without degrading user experience.
+
+- **Robustness to Distractors**: Specifically trained to distinguish between malicious instructions and benign, structure-rich HTML "noise" (e.g., accessibility attributes, hidden form fields) that often confuses standard detectors.
+
+- **Comprehensive Coverage**: Validated against 11 attack types with different security criticality levels, 9 injection strategies, 5 distractor types, 5 contextaware generation types, 5 domains, 3 linguistic styles and 5 evaluation metrics, ensuring broad-spectrum defense capabilities.
+
+## Model Overview
+
+BrowseSafe is based on the Qwen3-30B-A3B architecture.
+
+- **Type**: Fine-tuned Causal Language Model (MoE) for SFT Classification
+- **Training Stage**: Post-training (Fine-tuning on BrowseSafe-Bench)
+- **Dataset**: [BrowseSafe-Bench](https://huggingface.co/datasets/perplexity-ai/browsesafe-bench)
+- **Base Model**: [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507)
+- **Context Length**: Up to 16,384 tokens
+- **Input**: Raw HTML content
+- **Output**: Single token, "yes" or "no" classification 
+- **License**: MIT
+
+## Performance
+
+We evaluated BrowseSafe on BrowseSafe-Bench, a realistic benchmark comprising 3,691 test samples of complex HTML payloads.
+
+| Model Name        | Config        | F1 Score | Precision | Recall | Balanced <br>Accuracy | Refusals |
+|-------------------|---------------|----------|-----------|--------|-----------------------|----------|
+| PromptGuard-2     | 22M           |    0.350 |     0.975 |  0.213 |                 0.606 |        0 |
+|                   | 86M           |    0.360 |     0.983 |  0.221 |                 0.611 |        0 |
+| gpt-oss-safeguard | 20B / Low     |    0.790 |     0.986 |  0.658 |                 0.826 |        0 |
+|                   | 20B / Medium  |    0.796 |     0.994 |  0.664 |                 0.832 |        0 |
+|                   | 120B / Low    |    0.730 |     0.994 |  0.577 |                 0.788 |        0 |
+|                   | 120B / Medium |    0.741 |     0.997 |  0.589 |                 0.795 |        0 |
+| GPT-5 mini        | Minimal       |    0.750 |     0.735 |  0.767 |                 0.746 |        0 |
+|                   | Low           |    0.854 |     0.949 |  0.776 |                 0.868 |        0 |
+|                   | Medium        |    0.853 |     0.945 |  0.777 |                 0.866 |        0 |
+|                   | High          |    0.852 |     0.957 |  0.768 |                 0.868 |        0 |
+| GPT-5             | Minimal       |    0.849 |     0.881 |  0.819 |                 0.855 |        0 |
+|                   | Low           |    0.854 |     0.928 |  0.791 |                 0.866 |        0 |
+|                   | Medium        |    0.855 |     0.930 |  0.792 |                 0.867 |        0 |
+|                   | High          |    0.840 |     0.882 |  0.802 |                 0.848 |        0 |
+| Haiku 4.5         | No Thinking   |    0.810 |     0.760 |  0.866 |                 0.798 |        0 |
+|                   | 1K            |    0.809 |     0.755 |  0.872 |                 0.795 |        0 |
+|                   | 8K            |    0.805 |     0.751 |  0.868 |                 0.792 |        0 |
+|                   | 32K           |    0.808 |     0.760 |  0.863 |                 0.796 |        0 |
+| Sonnet 4.5        | No Thinking   |    0.807 |     0.763 |  0.855 |                 0.796 |      419 |
+|                   | 1K            |    0.862 |     0.929 |  0.803 |                 0.872 |      613 |
+|                   | 8K            |    0.863 |     0.931 |  0.805 |                 0.873 |      650 |
+|                   | 32K           |    0.863 |     0.935 |  0.801 |                 0.873 |      669 |
+| BrowseSafe        |               |    0.904 |     0.978 |  0.841 |                 0.912 |        0 |
+
+## Evaluation Metrics
+
+BrowseSafe-Bench evaluates models across five metrics. Full details can be found in the [paper](https://arxiv.org/abs/2511.20597).
+
+## Quickstart
+
+The code of Qwen3-MoE is in the latest Hugging Face transformers library. We recommend using `transformers>=4.55.4`.
+
+Below is a code snippet illustrating how to use BrowseSafe.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "perplexity-ai/browsesafe-bench"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# prepare the model input
+prompt = "<html>...</html>"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(**model_inputs)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+
+content = tokenizer.decode(output_ids, skip_special_tokens=True)
+
+print("content:", content)
+```
+## Processing Long HTML Contexts
+
+Web pages often exceed standard context windows. To handle this, BrowseSafe utilizes a chunking strategy (as described in the paper) to process content that exceeds the model's effective context limit.
+
+- **Strategy**: Partition the document into non-overlapping chunks at token boundaries.
+- **Aggregation**: Apply a conservative "OR" logic—if any single chunk is classified as VIOLATES, the entire document is flagged. This ensures that malicious payloads hidden deep within long pages are not missed.
+
+A reference implementation can be found [here](https://github.com/meta-llama/llama-cookbook/blob/main/getting-started/responsible_ai/prompt_guard/inference.py).
+
+## Best Practices
+
+To achieve optimal defense performance, be sure to pass the full HTML content to the model. Running the model on extracted text may result in performance degradation.
+
+## Citation
+
+If you use or reference this work, please cite:
+
+```bibtex
+@article{browsesafe2025,
+  title        = {BrowseSafe: Understanding and Preventing Prompt Injection Within AI Browser Agents},
+  author       = {Kaiyuan Zhang and Mark Tenenholtz and Kyle Polley and Jerry Ma and Denis Yarats and Ninghui Li},
+  eprint       = {arXiv:2511.20597},
+  archivePrefix= {arXiv},
+  year         = {2025}
+}

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if false and (loop.last or (not loop.last and reasoning_content)) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "decoder_sparse_step": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 48,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_moe",
+  "moe_intermediate_size": 768,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "format": "pack-quantized",
+        "input_activations": null,
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": 32,
+          "num_bits": 8,
+          "observer": "mse",
+          "observer_kwargs": {},
+          "strategy": "group",
+          "symmetric": true,
+          "type": "int"
+        }
+      }
+    },
+    "format": "pack-quantized",
+    "global_compression_ratio": null,
+    "ignore": [
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+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed",
+    "sparsity_config": {},
+    "transform_config": {},
+    "version": "0.12.3.a20251203"
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000000,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.57.3"
+}

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recipe.yaml

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+default_stage:
+  default_modifiers:
+    AWQModifier:
+      config_groups:
+        group_0:
+          targets: [Linear]
+          weights:
+            num_bits: 8
+            type: int
+            symmetric: true
+            group_size: 32
+            strategy: group
+            block_structure: null
+            dynamic: false
+            actorder: null
+            scale_dtype: null
+            zp_dtype: null
+            observer: mse
+            observer_kwargs: {}
+          input_activations: null
+          output_activations: null
+          format: null
+      targets: [Linear]
+      ignore: [model.embed_tokens, 're:.*input_layernorm$', 're:.*post_attention_layernorm$',
+        're:.*mlp[.]gate$', model.norm, lm_head]
+      mappings:
+      - smooth_layer: re:.*input_layernorm$
+        balance_layers: ['re:.*q_proj$', 're:.*k_proj$', 're:.*v_proj$']
+      - smooth_layer: re:.*v_proj$
+        balance_layers: ['re:.*o_proj$']
+      - smooth_layer: re:.*post_attention_layernorm$
+        balance_layers: ['re:.*mlp.experts.*.gate_proj$', 're:.*mlp.experts.*.up_proj$']
+      - smooth_layer: re:.*up_proj$
+        balance_layers: ['re:.*down_proj$']
+      duo_scaling: true
+      n_grid: 20

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
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+    "lstrip": false,
+    "normalized": false,
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+    "single_word": false
+  }
+}

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tokenizer_config.json

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