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Model: fdtn-ai/Foundation-Sec-1.1-8B-Instruct Source: Original Platform
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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-1.1-8B-Instruct - Model Card
+
+## Model Information
+
+Llama-3.1-FoundationAI-SecurityLLM-1.1-8B-Instruct (Foundation-Sec-1.1-8B-Instruct) is an open-weight, 8-billion parameter instruction-tuned language model specialized for cybersecurity applications.
+It extends the Foundation-Sec-1.1-8B base model with instruction-following capabilities and **extended 64k context window support**.
+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-1.1-8B-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-1.1-8B-Instruct (Foundation-Sec-1.1-8B-Instruct)
+- **Extended context window:** Increased from 4k to 64k tokens, enabling processing of longer security documents, incident reports, and threat intelligence feeds
+- **Model Developer:** Foundation AI at Cisco
+- **Model Card Contact:** [https://fdtn.ai/contact](https://fdtn.ai/contact)
+- **Model Release Date:** November 20, 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](https://huggingface.co/fdtn-ai/Foundation-Sec-1.1-8B-Instruct/blob/main/NOTICE.md)
+
+## Intended Use
+
+### Intended Use Cases
+
+Foundation-Sec-1.1-8B-Instruct is designed for security practitioners, researchers, and developers building AI-powered security workflows and applications.
+Foundation-Sec-1.1-8B-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-1.1-8B-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-1.1-8B-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.
+[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-1.1-8B-Instruct")
+model = AutoModelForCausalLM.from_pretrained("fdtn-ai/Foundation-Sec-1.1-8B-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-1.1-8B-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-1.1-8B-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
+- **65,536-token** sequence length
+- **Optimizer:** AdamW
+
+A more detailed description of the methodology is available in the technical report.
+
+### Evaluation
+
+Foundation-Sec-1.1-8B-Instruct was benchmarked on cybersecurity and general reasoning tasks, using a standardized 0-shot instruction prompting setup (temperature = 0.3).
+
+| **Benchmark** | **Foundation-sec-1.1-8B** | **Llama 3.1 8B** | **GPT-4o-mini** |
+| --- | --- | --- | --- |
+| CTI-MCQA | 0.644 | 0.617 | 0.672 |
+| CTI-RCM | 0.694 | 0.558 | 0.655 |
+| CTI-VSP | 0.865 | 0.815 | 0.792 |
+| IF-Eval | 0.815 | 0.791 | 0.834 |
+| Alpaca Eval 2 | 32.643 | 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 +13 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-1.1-8B-Instruct | 94.74% |
+| **LlamaGuard** + Foundation-Sec-1.1-8B-Instruct | 98.5% |
+
+## Limitations
+
+Foundation-Sec-1.1-8B-Instruct has several limitations that users should be aware of:
+
+1. **Domain-specific knowledge limitations**:
+    - Foundation-Sec-1.1-8B-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-1.1-8B-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