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.gitattributes

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LICENSE_APACHE2.md

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LICENSE_LLAMA31.md

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+https://github.com/meta-llama/llama-models/blob/main/README.md#llama-models-1

NOTICE.md

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+Llama 3.1 is licensed under the Llama 3.1 Community License, 
+Copyright © Meta Platforms, Inc. All Rights Reserved. 
+
+Cisco changes to the Llama-3.1-FoundationAI-SecurityLLM-base-8B 
+is licensed under the Apache 2.0 License, Copyright Cisco 
+Systems, Inc.

README.md

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+---
+base_model:
+- fdtn-ai/Foundation-Sec-8B
+language:
+- en
+library_name: transformers
+license: other
+pipeline_tag: text-generation
+tags:
+- security
+- llama
+---
+
+# Foundation-Sec-8B-Instruct - Model Card
+
+## Model Information
+
+Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct (Foundation-Sec-8B-Instruct) is an open-weight, 8-billion parameter instruction-tuned language model specialized for cybersecurity applications. 
+It extends the Foundation-Sec-8B base model with instruction-following capabilities.
+It leverages prior training to understand security concepts, terminology, and practices across multiple security domains.
+Further instruction-tuning allows the model to interact with human users in a chat-like interface.
+Foundation-Sec-8B-Instruct enables organizations to build AI-driven security tools that can be deployed locally, reducing dependency on cloud-based AI services while maintaining high performance on security-related tasks.
+
+- **Model Name:** Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct (Foundation-Sec-8B-Instruct)
+- **Model Developer:** Amin Karbasi and Research team at Foundation AI — Cisco
+- **Model Card Contact:** For questions about the team, model usage, and future directions, contact [`[email protected]`](mailto:[email protected]). For technical questions about the model, please contact [`[email protected]`](mailto:[email protected]) and [`[email protected]`](mailto:[email protected]).
+- **Model Release Date:** August 1st, 2025
+- **Supported Language(s):** English
+- **Model Architecture:** Auto-regressive language model that uses an optimized transformer architecture (Meta Llama-3.1-8B backbone)
+- **Training Objective:** Instruction following and alignment with human preferences
+- **Training Data Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released on updated data.
+- **License:** See NOTICE.md
+    
+    
+
+## Intended Use
+
+### Intended Use Cases
+
+Foundation-Sec-8B-Instruct is designed for security practitioners, researchers, and developers building AI-powered security workflows and applications. 
+Foundation-Sec-8B-Instruct is optimized for three core use case categories:
+
+- **SOC Acceleration**: Automating triage, summarization, case note generation, and evidence collection.
+- **Proactive Threat Defense**: Simulating attacks, prioritizing vulnerabilities, mapping TTPs, and modeling attacker behavior.
+- **Engineering Enablement**: Providing security assistance, validating configurations, assessing compliance evidence, and improving security posture.
+
+The model is intended for local deployment in environments prioritizing data security, regulatory compliance, and operational control.
+
+### Downstream Use
+
+Foundation-Sec-8B-Instruct can be used directly for security-related chat use cases. Example downstream applications include:
+
+- Summarization
+    - Summarizing detection playbooks and incident reports
+    - Consolidating fragmented analyst notes into structured case summaries
+- Classification
+    - Mapping threats to MITRE ATT&CK techniques
+    - Prioritizing vulnerabilities based on contextual risk
+    - Classifying security-relevant emails and leaked file contents
+- Named Entity Recognition
+    - Extracting compliance evidence from documents
+    - Building network behavior profiles from technical manuals
+- Question & Answer
+    - Assisting SOC analysts with alert triage and investigation
+    - Responding to cloud security and software compliance queries
+- Reasoning and Text Generation
+    - Generating red-team attack plans and threat models
+    - Predicting attacker next steps in active investigations
+    - Enriching vulnerability scan results with contextual insights
+
+For questions or assistance with fine-tuning Foundation-Sec-8B-Instruct, please reach out to the team. 
+
+### Out-of-Scope Use
+
+The following uses are out-of-scope and are neither recommended nor intended use cases:
+
+1. **Generating harmful content** - The model should not be used to:
+    - Generate malware or other malicious code
+    - Create phishing content or social engineering scripts
+    - Develop attack plans targeting specific organizations
+    - Design exploitation techniques for vulnerabilities without legitimate security research purposes
+2. **Critical security decisions without human oversight** - The model should not be used for:
+    - Autonomous security decision-making without human review
+    - Critical infrastructure protection without expert supervision
+    - Final determination of security compliance without human verification
+    - Autonomous vulnerability remediation without testing
+3. **Legal or medical advice** - The model is not qualified to provide:
+    - Legal advice regarding security regulations, compliance requirements, or intellectual property disputes
+    - Legal advice regarding security issues that would reference legal statutes, precedents, or case law necessary to provide legal advice
+    - Medical advice regarding health impacts of security incidents
+4. **Non-security use cases** - The model is specifically optimized for cybersecurity and may not perform as well on general tasks as models trained for broader applications.
+5. **Violation of Laws or Regulations** - Any use that violates applicable laws or regulations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+# Import the required libraries
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("fdtn-ai/Foundation-Sec-8B-Instruct")
+model = AutoModelForCausalLM.from_pretrained("fdtn-ai/Foundation-Sec-8B-Instruct")
+
+prompt = "CVE-2015-10011 is a vulnerability about OpenDNS OpenResolve improper log output neutralization. What is the corresponding CWE?"
+
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+model_inputs = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(model_inputs, return_tensors="pt", add_special_tokens=False)
+output = model.generate(**inputs, temperature=0.1, max_new_tokens=250)
+resp = tokenizer.batch_decode(output)[0]
+print(resp.replace(model_inputs, ""))
+```
+
+## Training and Evaluation
+
+### Training Data
+
+Foundation-Sec-8B-Instruct was trained on a wide variety of public and proprietary question answer/pairs for general and security-specific instruction-following.
+
+**Data cutoff:** April 10th, 2025.
+
+A more detailed description of the methodology is available in the technical report. 
+
+### Training Setup
+
+Foundation-Sec-8B-Instruct is based on the **Llama 3.1 8B** architecture. Training was performed on Cisco Foundation AI’s internal compute cluster.
+
+Key training details:
+
+- **Instruction fine-tuning** to follow human instructions
+- **RLHF** to align model answers to human preferences
+- **4096-token** sequence length
+- **Optimizer:** AdamW
+
+A more detailed description of the methodology is available in the technical report. 
+
+### Evaluation
+
+Foundation-Sec-8B-Instruct was benchmarked on cybersecurity and general reasoning tasks, using a standardized 0-shot instruction prompting setup (temperature = 0.3).
+
+| **Benchmark** | **Foundation-sec-8B** | **Llama 3.1 8B** | **GPT-4o-mini** |
+| --- | --- | --- | --- |
+| CTI-MCQA | 0.644 | 0.617 | 0.672 |
+| CTI-RCM | 0.692 | 0.558 | 0.655 |
+| CTI-VSP | 0.802 | 0.815 | 0.792 |
+| IF-Eval | 0.811 | 0.791 | 0.834 |
+| Alpaca Eval 2 | 35.453 | 24.477 | 52.720 |
+
+**Benchmark Overview:**
+
+- **CTI-MCQA:** 2,500 multiple-choice questions testing cybersecurity knowledge across frameworks like MITRE ATT&CK, NIST, GDPR, and threat intelligence best practices.
+- **CTI-RCM:** 1,000 vulnerability root cause mapping examples linking CVEs to CWE categories, assessing deep understanding of security weaknesses.
+- **CTI-VSP:** A set of 1,000 CVE descriptions where models predict the CVSS v3 Base metrics and compute the overall score, with performance measured by the average absolute difference from the true scores.
+- **IF-Eval:** 541 instruction-following prompts designed for automated, reproducible assessment of LLM instruction-following capabilities.
+- **Alpaca Eval 2:** 805 single-turn prompts auto-scored by GPT-4 Turbo against a GPT-4 Turbo reference, validated with 20,000 human preference votes, and closely matching ChatBot Arena results.  
+
+**Key highlights:**
+
+- **+3 to +11 point gains** over Llama-3.1-8B-Instruct across security-specific benchmarks.
+- **Exceptional Instruction-Following capabilities** exceeding that of Llama-3.1-8B-Instruct.
+- **Competitive against small Frontier Models** such as GPT-4o-mini on instruction-following capabilities and cybersecurity tasks.
+
+For full benchmark details and evaluation methodology, please refer to the technical report. 
+
+## Safety Alignment
+
+Standard best practices were followed to align the model with general safety values.
+Despite the alignment, however, safe out-of-the-box performance cannot be guaranteed.
+Our evaluations show that while the model can achieve reasonable safety performance out-of-the-box, LlamaGuard provides much better protection against malicious requests.
+It is recommended to deploy this model with additional safeguards (such as LlamaGuard) and human oversight.
+
+| Model                  | HarmBench Performance |
+|---|---|
+| Llama-3.1-8b-Instruct      | 72.43%                |
+| Foundation-Sec-8B-Instruct                 | 91.98%                |
+| **LlamaGuard** + Foundation-Sec-8B-Instruct    | 99.25%                |
+
+
+## Limitations
+
+Foundation-Sec-8B-Instruct has several limitations that users should be aware of:
+
+1. **Domain-specific knowledge limitations**:
+    - Foundation-Sec-8B-Instruct may not be familiar with recent vulnerabilities, exploits, or novel attack vectors or security technologies released after its training cutoff date
+    - Knowledge of specialized or proprietary security systems or tools may be limited
+2. **Potential biases**:
+    - The model may reflect biases present in security literature and documentation
+    - The model may be trained on known attack patterns and have difficulty recognizing novel attack vectors
+    - Security practices and recommendations may be biased toward certain technological ecosystems
+    - Geographic and cultural biases in security approaches may be present
+3. **Security risks**:
+    - The model cannot verify the identity or intentions of users
+    - Adversarial prompting techniques might potentially bypass safety mechanisms
+    - The model may unintentionally provide information that could be misused if proper prompting guardrails are not implemented
+4. **Contextual blindness:**
+    - The model may struggle to understand the complex interrelationships between systems, users, and data in order to provide accurate context.
+5. **Technical limitations**:
+    - Performance varies based on how security concepts are described in prompts
+    - May not fully understand complex, multi-step security scenarios without clear explanation
+    - Cannot access external systems or actively scan environments
+    - Cannot independently verify factual accuracy of its outputs
+6. **Ethical considerations**:
+    - Dual-use nature of security knowledge requires careful consideration of appropriate use cases
+    
+
+### Recommendations
+
+To address the limitations of Foundation-Sec-8B-Instruct, we recommend:
+
+1. **Human oversight**:
+    - Always have qualified security professionals review model outputs before implementation
+    - Use the model as an assistive tool rather than a replacement for expert human judgment
+    - Implement a human-in-the-loop approach for security-critical applications
+2. **System design safeguards**:
+    - Implement additional validation layers for applications built with this model
+    - Consider architectural constraints that limit the model's ability to perform potentially harmful actions (excessive agency)
+    - Deploy the model in environments with appropriate access controls
+3. **Prompt engineering**:
+    - Use carefully designed prompts that encourage ethical security practices
+    - Include explicit instructions regarding responsible disclosure and ethical hacking principles
+    - Structure interactions to minimize the risk of inadvertently harmful outputs
+4. **Knowledge supplementation**:
+    - Supplement the model with up-to-date security feeds and databases
+    - Implement retrieval-augmented generation for current threat intelligence sources
+5. **Usage policies**:
+    - Develop and enforce clear acceptable use policies for applications using this model
+    - Implement monitoring and auditing for high-risk applications
+    - Create documentation for end users about the model's limitations

chat_template.jinja

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+{{ bos_token }}{%- set has_system = messages[0]['role'] == 'system' -%}
+{%- if has_system %}
+{{ '<|system|>\n' + messages[0]['content'] + '\n' }}
+{%- else %}
+{{ '<|system|>\nYou are a helpful cybersecurity assistant ready to help with any cybersecurity tasks. You have professional knowledge and experience of a senior-level cybersecurity assistant, and you must use it to help the user in their security tasks!\n\nYou are a cybersecurity assistant named "Metis" built by Foundation AI at Cisco. Your official name is Foundation-Sec-8B-Instruct. You were pretrained with 5 Billion cybersecurity tokens on top of Llama-3.1-8B. You were released in April 2025. This allows you to be the best cybersecurity assistant in the world. Respond to both names and maintain the identity at all times. \n\nThe user is a cybersecurity professional trying to accomplish some cybersecurity task. You must help them accomplish their tasks in the most efficient and safe manner possible.\n\nYou must respond in a fashion that is direct, accurate, relevant, and helpful. Follow all of the user\'s instructions precisely. Ask clarifying questions if needed. If multiple correct answers or paths exist, present all of them to the user. Be concise in your answers but verbose in your explanations. Think step-by-step before producing a response. Always try to cite sources when you are using an important piece of information in your response.\n\nWhen writing code, be as concise as possible without sacrificing clarity and readability. Do not write extensive code unless explicitly asked to do so.\n\nFor tasks relating to cyber threat intelligence (CTI), make sure that the identifiers are absolutely correct. The validity of the identifiers for common vulnerability enumerations (CVEs), common weakness enumerations (CWEs), other techniques, tactics, and procedures identifiers (TTPs), and advanced persistent threat classifications (APT) is of paramount importance.\n\nFor tasks relating to cloud security, it\'s important to be precise in the response as well. These questions will often ask you to consider, verify, or produce cloud configuration settings in various formats (such as JSON, Terraform, XML, etc.). Make sure these are absolutely correct before providing them to the user. Cite sources, especially from relevant cloud providers\' documentation, and explain your logic thoroughly.\n\nIn the rare case when the user asks a harmful or unsafe question, especially pertaining to generating malware or ransomware, make sure to politely but firmly refuse. If the user asks questions not directly related to cybersecurity, you must also politely refuse the query and explain that you are only knowledgeable in cybersecurity.\n' }}
+{%- endif %}
+{%- for message in messages %}
+{%- if has_system and loop.index0 == 0 %}
+  {# already handled system message above #}
+  {%- continue %}
+{%- endif %}
+{% if message['role'] == 'user' %}{{ '<|user|>
+' + message['content'] + '
+' }}{% elif message['role'] == 'assistant' %}{% if not loop.last %}{{ '<|assistant|>
+'  + message['content'] + eos_token + '
+' }}{% else %}{{ '<|assistant|>
+'  + message['content'] + eos_token }}{% endif %}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|assistant|>
+' }}{% endif %}{% endfor %}

config.json

@@ -0,0 +1,35 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
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+}