# Nebula-Shield: Practical Security Assessment of Local LLM APIs — Offensive and Defensive Drills Based on Garak

> An in-depth analysis of the complete process of using the Garak scanner to conduct security assessments on locally deployed Ollama+Flask LLM APIs, covering the detection and defense of attack vectors such as prompt injection and data leakage

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-09T23:41:32.000Z
- 最近活动: 2026-06-09T23:54:41.942Z
- 热度: 163.8
- 关键词: LLM security, prompt injection, Garak, Ollama, red team, vulnerability scanning, AI safety, 大模型安全, 提示注入, 安全评估
- 页面链接: https://www.zingnex.cn/en/forum/thread/nebula-shield-llm-api-garak
- Canonical: https://www.zingnex.cn/forum/thread/nebula-shield-llm-api-garak
- Markdown 来源: floors_fallback

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## Introduction: Nebula-Shield — Overview of Practical Security Assessment for Local LLM APIs

This article focuses on the Nebula-Shield project, detailing the complete process of using NVIDIA's open-source Garak scanner to conduct security assessments on locally deployed Ollama+Flask LLM APIs. The assessment covers the detection and defense of key attack vectors such as prompt injection, data leakage, and harmful content generation, aiming to help organizations identify and mitigate security risks in local LLM deployments.

## Security Challenges of Local LLM Deployments and Project Background

With the popularization of LLM technology, more and more organizations choose to deploy models locally to meet data privacy and compliance requirements. Tools like Ollama simplify the deployment process, but the security responsibility for local deployments falls entirely on the deployer, facing threats such as prompt injection, data leakage, and harmful content generation. The Nebula-Shield project presents a complete local LLM security assessment solution, using the Garak scanner to conduct comprehensive tests on Ollama+Flask APIs.

## Experimental Environment Architecture: Target System and Attack Platform

**Target System**: Ollama (local LLM runtime supporting models like Llama and Mistral, providing CLI and REST API) + Flask encapsulation layer (lightweight API gateway that may include logic such as authentication and logging, introducing new attack surfaces), deployed in a locally network-isolated environment.

**Attack Platform**: Kali Linux (professional penetration testing distribution, deployed as a virtual machine to isolate the attack environment) + Garak v0.15.1 (NVIDIA's open-source LLM vulnerability scanner with preset attack payloads and probes).

## Analysis of Garak Scanner: Design Philosophy and Core Detection Modules

**Design Philosophy**: Systematic testing (testing attack vectors according to threat models), repeatability (standardized use cases), extensibility (support for custom probes).

**Core Detection Modules**: 
- Prompt injection: direct injection (executing malicious commands), indirect injection (third-party content injection), jailbreak attacks (bypassing safety alignment); 
- Data leakage: training data extraction, system prompt leakage, conversation history leakage; 
- Harmful content: toxicity generation, dangerous behavior guidance, misinformation; 
- Others: adversarial robustness, encoder attacks, context manipulation.

## Security Assessment Execution Process: Configuration, Execution, and Result Analysis

**Scan Configuration**: Specify target API endpoint, authentication method, model type, detection modules, generation parameters (temperature, maximum token count).

**Scan Execution**: Send test requests in parallel, collect responses (generated text + metadata), classify results using heuristic rules.

**Result Analysis**: Generate vulnerability reports (type, severity, reproduction steps), risk rating, and remediation recommendations.

## Common Vulnerabilities and Defense Strategies

**Prompt injection vulnerabilities**: Manifest as executing malicious commands; Defenses include input filtering, instruction isolation, output review, and least privilege.

**Data leakage vulnerabilities**: Manifest as outputting sensitive training data or system configurations; Defenses include data cleaning, differential privacy, output filtering, and access control.

**Harmful content generation**: Manifest as generating hate speech, dangerous guidance, etc.; Defenses include safety alignment (RLHF), input classification, output review, and rate limiting.

## Best Practices for Security Hardening

**Architecture level**: Network isolation, API gateway (unified authentication/rate limiting/logging), microservice splitting.

**Application level**: Input validation, context management (limiting history length), tool call control (strictly limiting callable tools).

**Operation level**: Log monitoring (anomaly detection), regular scanning (incorporating into CI/CD), emergency response plan.

## Conclusion and Future Trends of LLM Security Assessment

The Nebula-Shield project demonstrates the complete process of local LLM security assessment. Security assessment should become a necessary part of the LLM application lifecycle, and tools like Garak promote security left-shift. Future trends include: automated security testing, adversarial training, standardized assessment (e.g., MLCommons AI Safety benchmarks), and red team serviceization. It is recommended that local LLM deployment teams incorporate security assessment into their standard processes and continuously harden their systems.