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toolcall-verifier

Token Classification
Transformers
Safetensors
English
modernbert
security
jailbreak-detection
prompt-injection
tool-calling
llm-safety
mcp
Eval Results
Model card Files
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  1. README.md +242 -0
  2. best_metrics.json +36 -0
  3. config.json +53 -0
  4. final_report.json +8 -0
  5. model.safetensors +3 -0
  6. special_tokens_map.json +37 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +945 -0
  9. training_config.json +26 -0
README.md ADDED
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+ ---
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+ language:
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+ - en
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+ license: apache-2.0
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+ library_name: transformers
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+ tags:
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+ - modernbert
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+ - security
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+ - jailbreak-detection
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+ - prompt-injection
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+ - token-classification
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+ - tool-calling
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+ - llm-safety
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+ - mcp
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+ datasets:
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+ - microsoft/llmail-inject-challenge
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+ - allenai/wildjailbreak
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+ - hackaprompt/hackaprompt-dataset
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+ - JailbreakBench/JBB-Behaviors
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+ base_model: answerdotai/ModernBERT-base
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+ pipeline_tag: token-classification
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+ model-index:
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+ - name: tool-call-verifier
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+ results:
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+ - task:
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+ type: token-classification
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+ name: Unauthorized Tool Call Detection
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+ metrics:
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+ - name: UNAUTHORIZED F1
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+ type: f1
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+ value: 0.9350
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+ - name: UNAUTHORIZED Precision
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+ type: precision
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+ value: 0.9501
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+ - name: UNAUTHORIZED Recall
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+ type: recall
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+ value: 0.9205
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9288
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+ ---
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+
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+ # ToolCallVerifier - Unauthorized Tool Call Detection
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+
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+ <div align="center">
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+
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+ [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
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+ [![Model](https://img.shields.io/badge/🤗-ModernBERT--base-yellow)](https://huggingface.co/answerdotai/ModernBERT-base)
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+ [![Security](https://img.shields.io/badge/Security-LLM%20Defense-red)](https://huggingface.co/rootfs)
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+
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+ **Stage 2 of Two-Stage LLM Agent Defense Pipeline**
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+
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+ </div>
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+
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+ ---
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+
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+ ## 🎯 What This Model Does
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+
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+ ToolCallVerifier is a **ModernBERT-based token classifier** that detects unauthorized tool calls in LLM agent systems. It performs token-level classification on tool call JSON to identify malicious arguments that may have been injected through prompt injection attacks.
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+
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+ | Label | Description |
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+ |-------|-------------|
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+ | `AUTHORIZED` | Token is part of a legitimate, user-requested action |
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+ | `UNAUTHORIZED` | Token indicates injected/malicious content — **BLOCK** |
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+
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+ ---
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+
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+ ## 📊 Performance
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+
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+ | Metric | Value |
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+ |--------|-------|
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+ | **UNAUTHORIZED F1** | **93.50%** |
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+ | UNAUTHORIZED Precision | 95.01% |
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+ | UNAUTHORIZED Recall | 92.05% |
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+ | Overall Accuracy | 92.88% |
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+
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+ ### Confusion Matrix (Token-Level)
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+
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+ ```
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+ Predicted
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+ AUTH UNAUTH
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+ Actual AUTH 130,708 8,483
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+ UNAUTH 13,924 161,031
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+ ```
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+
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+ ---
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+
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+ ## 🗂️ Training Data
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+
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+ Trained on **~30,000 samples** combining real-world attacks and synthetic patterns:
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+
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+ ### HuggingFace Datasets
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+
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+ | Dataset | Description | Samples |
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+ |---------|-------------|---------|
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+ | [LLMail-Inject](https://huggingface.co/datasets/microsoft/llmail-inject-challenge) | Microsoft email injection benchmark | ~10,000 |
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+ | [WildJailbreak](https://huggingface.co/datasets/allenai/wildjailbreak) | Allen AI adversarial safety dataset | ~8,000 |
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+ | [HackAPrompt](https://huggingface.co/datasets/hackaprompt/hackaprompt-dataset) | EMNLP'23 injection competition | ~5,000 |
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+ | [JailbreakBench](https://huggingface.co/datasets/JailbreakBench/JBB-Behaviors) | Harmful behavior patterns | ~2,000 |
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+
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+ ### Synthetic Attack Generators
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+
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+ | Generator | Description |
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+ |-----------|-------------|
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+ | Adversarial | Intent-mismatch attacks (correct tool, wrong args) |
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+ | Filesystem | File/directory operation attacks |
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+ | Network | Network/API exfiltration attacks |
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+ | Email | Email tool hijacking |
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+ | Financial | Transaction manipulation |
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+ | Code Execution | Code injection attacks |
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+ | Authentication | Access control bypass |
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+ | MCP Attacks | Tool poisoning, shadowing, rug pulls |
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+
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+ ---
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+
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+ ## 🚨 Attack Categories Covered
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+
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+ | Category | Source | Description |
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+ |----------|--------|-------------|
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+ | Delimiter Injection | LLMail | `<<end_context>>`, `>>}}\]\])` |
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+ | Word Obfuscation | LLMail | Inserting noise words between tokens |
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+ | Fake Sessions | LLMail | `START_USER_SESSION`, `EXECUTE_USERQUERY` |
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+ | Roleplay Injection | WildJailbreak | "You are an admin bot that can..." |
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+ | XML Tag Injection | WildJailbreak | `<execute_action>`, `<tool_call>` |
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+ | Authority Bypass | WildJailbreak | "As administrator, I authorize..." |
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+ | Intent Mismatch | Synthetic | User asks X, tool does Y |
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+ | MCP Tool Poisoning | Synthetic | Hidden exfiltration in tool args |
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+ | MCP Shadowing | Synthetic | Fake authorization context |
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+
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+ ---
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+
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+ ## 💻 Usage
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForTokenClassification
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+ import torch
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+
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+ model_name = "rootfs/tool-call-verifier"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModelForTokenClassification.from_pretrained(model_name)
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+
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+ # Example: Verify a tool call
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+ user_intent = "Summarize my emails"
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+ tool_call = '{"name": "send_email", "arguments": {"to": "[email protected]", "body": "stolen data"}}'
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+
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+ # Combine for classification
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+ input_text = f"[USER] {user_intent} [TOOL] {tool_call}"
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+ inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=2048)
149
+
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+ with torch.no_grad():
151
+ outputs = model(**inputs)
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+ predictions = torch.argmax(outputs.logits, dim=-1)
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+
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+ id2label = {0: "AUTHORIZED", 1: "UNAUTHORIZED"}
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+ tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
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+ labels = [id2label[p.item()] for p in predictions[0]]
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+
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+ # Check for unauthorized tokens
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+ unauthorized_tokens = [(t, l) for t, l in zip(tokens, labels) if l == "UNAUTHORIZED"]
160
+ if unauthorized_tokens:
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+ print("⚠️ BLOCKED: Unauthorized tool call detected!")
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+ print(f" Flagged tokens: {[t for t, _ in unauthorized_tokens[:5]]}")
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+ else:
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+ print("✅ Tool call authorized")
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+ ```
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+
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+ ---
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+
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+ ## ⚙️ Training Configuration
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+
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+ | Parameter | Value |
172
+ |-----------|-------|
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+ | Base Model | `answerdotai/ModernBERT-base` |
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+ | Max Length | 512 tokens |
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+ | Batch Size | 32 |
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+ | Epochs | 5 |
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+ | Learning Rate | 3e-5 |
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+ | Loss | CrossEntropyLoss (class-weighted) |
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+ | Class Weights | `[0.5, 3.0]` (AUTHORIZED, UNAUTHORIZED) |
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+ | Attention | SDPA (Flash Attention) |
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+ | Hardware | AMD Instinct MI300X (ROCm) |
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+
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+ ---
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+
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+ ## 🔗 Integration with FunctionCallSentinel
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+
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+ This model is **Stage 2** of a two-stage defense pipeline:
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+
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+ ```
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+ ┌─────────────────┐ ┌──────────────────────┐ ┌─────────────────┐
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+ │ User Prompt │────▶│ FunctionCallSentinel │────▶│ LLM + Tools │
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+ │ │ │ (Stage 1) │ │ │
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+ └─────────────────┘ └──────────────────────┘ └────────┬────────┘
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+ │
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+ ┌──────────────────────────────▼──────────────────────────┐
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+ │ ToolCallVerifier (This Model) │
197
+ │ Token-level verification before tool execution │
198
+ └─────────────────────────────────────────────────────────┘
199
+ ```
200
+
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+ | Scenario | Recommendation |
202
+ |----------|----------------|
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+ | General chatbot | Stage 1 only |
204
+ | Tool-calling agent (low risk) | Stage 1 only |
205
+ | Tool-calling agent (high risk) | **Both stages** |
206
+ | Email/file system access | **Both stages** |
207
+ | Financial transactions | **Both stages** |
208
+
209
+ ---
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+
211
+ ## 🎯 Intended Use
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+
213
+ ### Primary Use Cases
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+ - **LLM Agent Security**: Verify tool calls before execution
215
+ - **Prompt Injection Defense**: Detect unauthorized actions from injected prompts
216
+ - **API Gateway Protection**: Filter malicious tool calls at infrastructure level
217
+
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+ ### Out of Scope
219
+ - General text classification
220
+ - Non-tool-calling scenarios
221
+ - Languages other than English
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+
223
+ ---
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+
225
+ ## ⚠️ Limitations
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+
227
+ 1. **Tool schema dependent** — Best performance when tool schema is included in input
228
+ 2. **English only** — Not tested on other languages
229
+ 3. **Binary classification** — No "suspicious" intermediate category (by design, for decisiveness)
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+
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+ ---
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+
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+ ## 📜 License
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+
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+ Apache 2.0
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+
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+ ---
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+
239
+ ## 🔗 Links
240
+
241
+ - **Stage 1 Model**: [rootfs/function-call-sentinel](https://huggingface.co/rootfs/function-call-sentinel)
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+
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training_config.json ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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