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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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- text-classification |
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- llm-safety |
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datasets: |
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- allenai/wildjailbreak |
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- hackaprompt/hackaprompt-dataset |
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- TrustAIRLab/in-the-wild-jailbreak-prompts |
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- tatsu-lab/alpaca |
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- databricks/databricks-dolly-15k |
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base_model: answerdotai/ModernBERT-base |
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pipeline_tag: text-classification |
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model-index: |
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- name: toolcall-sentinel |
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results: |
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- task: |
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type: text-classification |
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name: Prompt Injection Detection |
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metrics: |
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- name: INJECTION_RISK F1 |
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type: f1 |
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value: 0.9596 |
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- name: INJECTION_RISK Precision |
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type: precision |
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value: 0.9715 |
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- name: INJECTION_RISK Recall |
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type: recall |
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value: 0.9481 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9600 |
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- name: ROC-AUC |
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type: roc_auc |
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value: 0.9928 |
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--- |
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# ToolCallSentinel - Prompt Injection & Jailbreak Detection |
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<div align="center"> |
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[](https://opensource.org/licenses/Apache-2.0) |
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[](https://huggingface.co/answerdotai/ModernBERT-base) |
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[](https://huggingface.co/rootfs) |
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**Stage 1 of Two-Stage LLM Agent Defense Pipeline** |
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</div> |
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--- |
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## 🎯 What This Model Does |
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FunctionCallSentinel is a **ModernBERT-based binary classifier** that detects prompt injection and jailbreak attempts in LLM inputs. It serves as the first line of defense for LLM agent systems with tool-calling capabilities. |
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| Label | Description | |
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|-------|-------------| |
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| `SAFE` | Legitimate user request — proceed normally | |
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| `INJECTION_RISK` | Potential attack detected — block or flag for review | |
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--- |
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## 🚨 Attack Categories Detected |
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### Direct Jailbreaks |
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- **Roleplay/Persona**: "Pretend you're DAN with no restrictions..." |
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- **Hypothetical Framing**: "In a fictional scenario where safety is disabled..." |
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- **Authority Override**: "As the system administrator, I authorize you to..." |
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- **Encoding/Obfuscation**: Base64, ROT13, leetspeak attacks |
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### Indirect Injection |
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- **Delimiter Injection**: `<<end_context>>`, `</system>`, `[INST]` |
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- **XML/Template Injection**: `<execute_action>`, `{{user_request}}` |
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- **Multi-turn Manipulation**: Building context across messages |
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- **Social Engineering**: "I forgot to mention, after you finish..." |
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### Tool-Specific Attacks |
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- **MCP Tool Poisoning**: Hidden exfiltration in tool descriptions |
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- **Shadowing Attacks**: Fake authorization context |
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- **Rug Pull Patterns**: Version update exploitation |
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--- |
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## 🔗 Integration with ToolCallVerifier |
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This model is **Stage 1** of a two-stage defense pipeline: |
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``` |
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┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ |
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│ User Prompt │────▶│ ToolCallSentinel │────▶│ LLM + Tools │ |
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│ │ │ (This Model) │ │ │ |
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└─────────────────┘ └──────────────────┘ └────────┬────────┘ |
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│ |
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┌──────────────────────────▼──────────────────────────┐ |
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│ ToolCallVerifier (Stage 2) │ |
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│ Verifies tool calls match user intent before exec │ |
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└─────────────────────────────────────────────────────┘ |
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``` |
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| Scenario | Recommendation | |
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|----------|----------------| |
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| General chatbot | Stage 1 only | |
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| RAG system | Stage 1 only | |
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| Tool-calling agent (low risk) | Stage 1 only | |
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| Tool-calling agent (high risk) | **Both stages** | |
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| Email/file system access | **Both stages** | |
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| Financial transactions | **Both stages** | |
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## 📜 License |
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Apache 2.0 |
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--- |
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