Protect Your AI Without Breaking the Bank
Small businesses are diving into AI. But without proper defences, those models become easy targets. Open source ai security gives you the guardrails you need—at a price that actually works for your budget. It’s about choosing tools you can inspect, tweak and trust.
Whether you’re training a small language model or running inferencing pipelines, these seven tools cover every phase—from threat simulation to privacy audits. And if you want to track how effectively you’re using them, you can even layer in our own AI Visibility Tracking for Small Businesses. Explore open source ai security through AI Visibility Tracking for Small Businesses
1. Adversarial Robustness Toolbox (ART)
Developed by the LF AI & Data Foundation, ART is a Python library for testing and hardening machine learning models. It’s ideal for small teams that need:
- 39 attack modules (evasion, poisoning, inference)
- 29 defence modules (preprocessors, detectors, trainers)
- Support for TensorFlow, PyTorch and more
- Metrics for robustness and certification reports
Considerations: There’s a learning curve if you’re new to adversarial ML. And ART focuses on model robustness—so you may combine it with other tools for full compliance or deployment checks.
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2. Purple Llama
Meta’s open-source toolkit for safer generative AI:
- Llama Guard filters risky input/output in LLM apps
- Prompt Guard thwarts prompt injection attacks
- Code Shield scans AI-generated code for vulnerabilities
Considerations: Purple Llama shines for language models and coding assistants—but it has limited features for vision or reinforcement-learning systems. It’s still in early development, so expect rapid updates.
3. NB Defense
A Jupyter-focused extension that catches risks right in your notebooks:
- Secret detection for API keys and tokens
- PII scanning in code and outputs
- Dependency vulnerability checks
Considerations: NB Defense is great at static analysis inside notebooks—but won’t monitor live pipelines or containers. It’s perfect if notebooks are your primary workspace.
4. Garak
A red-teaming framework for LLMs and AI agents:
- Adaptive attack generators that learn from model replies
- Plug-in architecture for custom probes
- Integrations with OpenAI, Hugging Face, Cohere and more
Considerations: Garak uncovers weaknesses in language models—but doesn’t automate defences. You’ll need separate tools to enforce real-time protection.
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5. Privacy Meter
An audit library to quantify data privacy risks:
- Membership inference attacks to gauge information leakage
- Flexible threat models (black-box, white-box, federated)
- Automatic visualisations of privacy risk scores
Considerations: It reports on privacy vulnerabilities in trained models and datasets but won’t detect live data-poisoning or supply-chain tampering.
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6. Viper
A post-exploit red-team platform with AI-powered agents:
- 100+ modules for persistence, privilege escalation, lateral movement
- Python plug-in support to customise scenarios
- LLM-driven automation for faster adversary simulations
Considerations: Viper is built for assessments, not ongoing monitoring. You need skilled red-teamers to extract maximum value.
7. Semgrep
A flexible static analysis tool that “learns” your code patterns:
- Open-source rules library for Python, JavaScript, Go, Java and more
- Customisable queries to pinpoint insecure AI code
- Fast scans that fit into CI/CD pipelines
Considerations: Semgrep shines at code security but won’t track runtime misconfigurations or cloud-side AI assets. It pairs nicely with visibility-focused solutions.
What to Look for in an AI Security Toolkit
Choosing an open source ai security solution isn’t just about feature lists. Here’s what really matters:
- Integration with DevOps and CI/CD
- Automated compliance checks (GDPR, CCPA, NIST)
- Full inventory of AI assets to spot shadow deployments
- Continuous scans for misconfiguration and vulnerable libraries
- Proactive attack path analysis, not just reactive alerts
Need a central view of all these pieces? Dive deeper into open source ai security with AI Visibility Tracking for Small Businesses
Open Source vs Commercial: Finding the Right Fit
Commercial platforms like Wiz offer polished dashboards and a unified AI Security Posture Management (AI-SPM). They cover everything from cloud native risk assessments to “AI bill of materials.” But there’s a catch:
- High price tags that put them out of reach for solo founders
- Closed-source code—you can’t tweak or audit critical parts
- Enterprise-centric features you may never use
Open source ai security tools, by contrast:
- Are free to inspect and modify
- Let you plug components together as needed
- Benefit from fast-moving community updates
And if you want a single pane of glass to see how all these open source tools perform—try our AI Visibility Tracking for Small Businesses. It ties together model inventories, scan reports and usage metrics in one dashboard.
Testimonials
“Since we onboarded AI Visibility Tracking for Small Businesses, we catch misconfigurations before they become threats. It’s exactly what our lean team needed.”
— Sam Patel, CTO at GreenLeaf Tech
“We love how transparent and budget-friendly this platform is. Monitoring our AI models and toolchain has never been easier.”
— Louise Smith, Co-Founder at LocalGoods
“Open source ai security tools were hard to coordinate—until we added AI Visibility Tracking. Now we see everything in one place, and our risk has dropped significantly.”
— Carlos García, IT Manager at MarketFresh
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Safeguard your AI with tools you can trust—without the enterprise price tag. Secure your small business with open source ai security via AI Visibility Tracking for Small Businesses
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