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

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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.

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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

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{% for message in messages %}{% if message['role'] == 'system' %}{{ '<|system|>
' + message['content'] + '
' }}{% elif 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 %}

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"architectures": [
"LlamaForCausalLM"
],
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"attention_dropout": 0.0,
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"hidden_act": "silu",
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"initializer_range": 0.02,
"intermediate_size": 14336,
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"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
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"high_freq_factor": 4.0,
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"original_max_position_embeddings": 8192,
"rope_type": "llama3"
},
"rope_theta": 500000,
"tie_word_embeddings": false,
"transformers_version": "4.57.1",
"use_cache": true,
"vocab_size": 128264
}

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