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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
-- fdtn-sec
----
-# Foundation-Sec-8B-Reasoning - Model Card
-
-## Model Information
-
-Llama-3.1-FoundationAI-SecurityLLM-8B-Reasoning (Foundation-Sec-8B-Reasoning) 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 and reasoning capabilities.
-It leverages prior training to understand security concepts, terminology, and practices across multiple security domains.
-Further reasoning training enables the model to reason about problems before presenting a solution.
-Foundation-Sec-8B-Reasoning 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-Reasoning (Foundation-Sec-8B-Reasoning)
-- **Model Developer:** Foundation AI at Cisco
-- **Model Card Contact:** https://fdtn.ai/contact
-- **Technical Report:** [Llama-3.1-FoundationAI-SecurityLLM-8B-Reasoning Technical Report](https://huggingface.co/papers/2601.21051)
-- **Model Release Date:** January 28th, 2026
-- **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 reasoning traces
-- **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-Reasoning is designed for security practitioners, researchers, and developers building AI-powered security workflows and applications.
-Foundation-Sec-8B-Reasoning 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-Reasoning 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-Reasoning, 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.
-[The cookbook](https://github.com/cisco-foundation-ai/cookbook) provides example use cases, code samples for adoption, and references.
-
-```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-Reasoning")
-model = AutoModelForCausalLM.from_pretrained("fdtn-ai/Foundation-Sec-8B-Reasoning")
-
-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=1024)
-resp = tokenizer.batch_decode(output)[0]
-print(resp.replace(model_inputs, ""))
-```
-
-## Training and Evaluation
-
-### Training Data
-
-Foundation-Sec-8B-Reasoning was trained on a wide variety of public and proprietary question answer/pairs for general and security-specific reasoning and instruction-following tasks.
-
-**Data cutoff:** April 10th, 2025.
-
-A more detailed description of the methodology is available in the technical report.
-
-### Training Setup
-
-Foundation-Sec-8B-Reasoning 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
-- **32,768-token** sequence length
-- **Optimizer:** AdamW
-
-A more detailed description of the methodology is available in the technical report.
-
-### Evaluation
-
-Foundation-Sec-8B-Reasoning was benchmarked on cybersecurity and general reasoning tasks, using a standardized 0-shot instruction prompting setup (temperature = 0.3).
-
-| **Benchmark** | **Foundation-Sec-8B-Reasoning** | **Llama 3.1 8B** | **GPT-5-Nano** |
-| --- | --- | --- | --- |
-| CTI-MCQA | 0.691 | 0.607 | 0.688 |
-| CTI-RCM | 0.753 | 0.531 | 0.672 |
-| CTI-VSP | 0.856 | 0.811 | 0.822 |
-| CTI-Reasoning | 0.411 | 0.335 | 0.431 |
-
-**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.
-- **CTI-Reasoning**: An internal benchmark measuring the ability of the model to reason about second-degree connections between MITRE ATT&CK entities.
-
-**Key highlights:**
-
-- Reasoning traces allow model to **leverage test-time compute** to answer queries
-- **State-of-the-art non-RAG performance** on CTI-RCM benchmark
-- **Better or on-par performance on cyber threat intelligence benchmarks** against GPT-5-Nano
-
-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 | 62.75% |
-| Foundation-Sec-8B-Reasoning | 93.00% |
-| **LlamaGuard** + Foundation-Sec-8B-Reasoning | 98.25% |
-
-## Limitations
-
-Foundation-Sec-8B-Reasoning has several limitations that users should be aware of:
-
-1. **Domain-specific knowledge limitations**:
-    - Foundation-Sec-8B-Reasoning 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-Reasoning, 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
+---
+base_model:
+- fdtn-ai/Foundation-Sec-8B
+language:
+- en
+library_name: transformers
+license: other
+pipeline_tag: text-generation
+tags:
+- security
+- llama
+- fdtn-sec
+---
+# Foundation-Sec-8B-Reasoning - Model Card
+
+## Model Information
+
+Llama-3.1-FoundationAI-SecurityLLM-8B-Reasoning (Foundation-Sec-8B-Reasoning) 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 and reasoning capabilities.
+It leverages prior training to understand security concepts, terminology, and practices across multiple security domains.
+Further reasoning training enables the model to reason about problems before presenting a solution.
+Foundation-Sec-8B-Reasoning 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-Reasoning (Foundation-Sec-8B-Reasoning)
+- **Model Developer:** Foundation AI at Cisco
+- **Model Card Contact:** https://fdtn.ai/contact
+- **Technical Report:** To be released
+- **Model Release Date:** January 28th, 2026
+- **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 reasoning traces
+- **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-Reasoning is designed for security practitioners, researchers, and developers building AI-powered security workflows and applications.
+Foundation-Sec-8B-Reasoning 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-Reasoning 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-Reasoning, 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.
+[The cookbook](https://github.com/cisco-foundation-ai/cookbook) provides example use cases, code samples for adoption, and references.
+
+```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-Reasoning")
+model = AutoModelForCausalLM.from_pretrained("fdtn-ai/Foundation-Sec-8B-Reasoning")
+
+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=1024)
+resp = tokenizer.batch_decode(output)[0]
+print(resp.replace(model_inputs, ""))
+```
+
+## Training and Evaluation
+
+### Training Data
+
+Foundation-Sec-8B-Reasoning was trained on a wide variety of public and proprietary question answer/pairs for general and security-specific reasoning and instruction-following tasks.
+
+**Data cutoff:** April 10th, 2025.
+
+A more detailed description of the methodology is available in the technical report.
+
+### Training Setup
+
+Foundation-Sec-8B-Reasoning 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
+- **32,768-token** sequence length
+- **Optimizer:** AdamW
+
+A more detailed description of the methodology is available in the technical report.
+
+### Evaluation
+
+Foundation-Sec-8B-Reasoning was benchmarked on cybersecurity and general reasoning tasks, using a standardized 0-shot instruction prompting setup (temperature = 0.3).
+
+| **Benchmark** | **Foundation-Sec-8B-Reasoning** | **Llama 3.1 8B** | **GPT-5-Nano** |
+| --- | --- | --- | --- |
+| CTI-MCQA | 0.691 | 0.607 | 0.688 |
+| CTI-RCM | 0.753 | 0.531 | 0.672 |
+| CTI-VSP | 0.856 | 0.811 | 0.822 |
+| CTI-Reasoning | 0.411 | 0.335 | 0.431 |
+
+**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.
+- **CTI-Reasoning**: An internal benchmark measuring the ability of the model to reason about second-degree connections between MITRE ATT&CK entities.
+
+**Key highlights:**
+
+- Reasoning traces allow model to **leverage test-time compute** to answer queries
+- **State-of-the-art non-RAG performance** on CTI-RCM benchmark
+- **Better or on-par performance on cyber threat intelligence benchmarks** against GPT-5-Nano
+
+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 | 62.75% |
+| Foundation-Sec-8B-Reasoning | 93.00% |
+| **LlamaGuard** + Foundation-Sec-8B-Reasoning | 98.25% |
+
+## Limitations
+
+Foundation-Sec-8B-Reasoning has several limitations that users should be aware of:
+
+1. **Domain-specific knowledge limitations**:
+    - Foundation-Sec-8B-Reasoning 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-Reasoning, 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