AI & Machine Learning

What is Machine Learning?

Understanding the basics of machine learning and its applications in security.

• Supervised vs. unsupervised learning
• Training, validation, and testing datasets
• Model evaluation and metrics
• Overfitting and underfitting concepts
• Feature engineering and selection
• Model deployment and inference

Neural Networks & Deep Learning

Explore neural networks and deep learning architectures for complex pattern recognition.

• Artificial neural network structure
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Transformers and attention mechanisms
• Backpropagation and training
• Transfer learning techniques

Large Language Models (LLMs)

Understanding large language models and transformer-based architectures.

• Transformer architecture overview
• Tokenization and embeddings
• Attention mechanisms explained
• Pre-training and fine-tuning
• Prompt engineering techniques
• Popular LLMs (GPT, Llama, Claude)

Python for Machine Learning

Essential Python libraries and tools for developing ML models.

• NumPy for numerical computing
• Pandas for data manipulation
• Scikit-learn for ML algorithms
• TensorFlow and Keras
• PyTorch framework
• Jupyter notebooks for experimentation

Data Preparation & Preprocessing

Preparing and cleaning data for machine learning model training.

• Data collection and sourcing
• Data cleaning and imputation
• Normalization and standardization
• Handling imbalanced datasets
• Data augmentation techniques
• Exploratory data analysis (EDA)

MLOps & Model Management

Operationalize machine learning models in production environments.

• Model versioning and tracking
• Model registry and artifact storage
• CI/CD for ML pipelines
• Model monitoring and drift detection
• A/B testing and canary deployments
• Containerization and orchestration

Adversarial Machine Learning

Attack techniques designed to fool or manipulate machine learning models.

• Adversarial examples and perturbations
• Evasion attacks on classifiers
• Poisoning attacks on training data
• Transferability of adversarial examples
• Black-box vs. white-box attacks
• Defense mechanisms and robustness

Prompt Injection Attacks

Exploiting language models through carefully crafted prompts and instructions.

• Direct prompt injection techniques
• Indirect prompt injection vectors
• Jailbreaking language models
• Data leakage through prompting
• Prompt injection prevention
• Input validation and sanitization

Model Extraction & Stealing

Techniques to steal or reverse-engineer machine learning models.

• Model extraction through queries
• Membership inference attacks
• Model inversion attacks
• API-based model stealing
• Model watermarking & fingerprinting
• Intellectual property protection

Data Privacy & Leakage

Risks of exposing sensitive information through AI models.

• Training data memorization
• Membership inference attacks
• Privacy leakage through outputs
• Differential privacy techniques
• Federated learning privacy
• GDPR compliance for ML

Model Bias & Fairness

Understanding and mitigating bias in machine learning systems.

• Sources of bias in ML models
• Fairness metrics and evaluation
• Algorithmic discrimination risks
• Bias in training data
• Debiasing techniques
• Fairness-aware ML approaches

Supply Chain & Dependency Risks

Security risks in pre-trained models and AI dependencies.

• Pre-trained model vulnerabilities
• Compromised model repositories
• Dependency vulnerabilities in packages
• Backdoors in ML libraries
• Model provenance and verification
• Secure model sourcing

Agentic AI Security

Security risks unique to autonomous AI agents with tool access and multi-step execution.

• Prompt injection in agentic systems
• Tool misuse and privilege escalation
• Autonomous action risks
• Multi-agent trust boundaries
• Principle of least privilege for agents
• Human-in-the-loop controls

Multimodal & Emerging Attacks

Vision-language models, audio AI, and the expanding attack surface of multi-modal systems.

• Visual prompt injection
• Audio and deepfake attacks
• LLM jailbreaking (2024–2025)
• Many-shot and crescendo attacks
• AI-generated phishing at scale
• Continuous red-teaming strategies

Intrusion Detection Systems

Use ML for network-based and host-based intrusion detection.

• Network traffic classification
• Anomaly detection algorithms
• Attack pattern recognition
• False positive reduction
• Real-time detection systems
• Integration with SIEM

Malware Analysis & Detection

Automated malware classification and threat analysis using ML.

• Binary analysis and disassembly
• Static malware detection
• Behavioral analysis
• Ransomware detection
• Zero-day malware identification
• Malware family clustering

User & Entity Behavior Analytics

Detect insider threats and compromised accounts through behavior analysis.

• User behavior profiling
• Anomalous activity detection
• Insider threat identification
• Account compromise detection
• Risk scoring and ranking
• Behavior baselining

Phishing & Social Engineering Detection

Identify phishing emails and social engineering attempts.

• Email content analysis
• URL and link detection
• Sender reputation analysis
• Natural language processing
• Credential harvesting detection
• User security training optimization

Vulnerability Prediction

Predict vulnerability existence and severity in code and systems.

• Code vulnerability detection
• Severity classification
• CVSS score prediction
• Vulnerability prioritization
• Patch recommendation
• Risk assessment automation

Automated Incident Response

Use ML in SOAR platforms for automated threat response.

• Alert correlation and enrichment
• Automated response actions
• Incident severity prediction
• Threat intelligence integration
• Playbook optimization
• Response time reduction

Model Layer

• Base model vulnerabilities — Unmodified foundation models contain inherent safety issues: training data memorization can leak PII, built-in biases affect outputs, and unaligned models may produce harmful content regardless of deployment context. Pre-trained models from model hubs (Hugging Face, ModelScope) carry supply chain risk if not vetted.
• Fine-tuning supply chain — Fine-tuning on uncurated datasets injects bias and vulnerabilities. Backdoored fine-tuning datasets can embed hidden triggers. The open-source fine-tuning ecosystem (LoRA adapters, QLoRA weights) has no standardized verification process.
• Model API attacks — Every deployed model API is a target for prompt injection, jailbreaking, and prompt leaking. Model APIs differ from traditional APIs: the "input validation" problem is semantic, not syntactic. Black-box API probing enables model extraction via adversarial queries.
• Model stealing via API — Attackers can reconstruct model behavior through systematic API queries (membership inference, model inversion) without ever seeing weights. Distillation attacks train surrogate models that replicate proprietary model behavior.

Data Layer

• Training data poisoning — Injecting malicious examples into training datasets during collection, cleaning, or augmentation. At web-scale (Common Crawl, LAION, The Pile), attackers can publish adversarial content that gets absorbed into training corpora.
• Vector database poisoning — RAG systems rely on vector databases that can be poisoned by inserting malicious embeddings. Attackers who can write to the knowledge base can redirect AI responses, exfiltrate data from other users' queries, or inject harmful content.
• Training data extraction — Models memorize training data including personal information, trade secrets, and copyrighted content. Membership inference attacks determine whether specific data was used in training, creating GDPR compliance risk.
• Data pipeline interception — Data flowing between collection, preprocessing, and training stages is vulnerable to interception and modification. Data versioning tools (DVC, LakeFS) lack standardized security controls.

Integration Layer

• Agent tool misuse — AI agents with tool access (browsing, code execution, file operations, API calls) can be manipulated into taking unintended actions. Agent frameworks (LangChain, AutoGen, CrewAI) have diverse tool interfaces with varying security models.
• Plugin/extension security — AI plugins and extensions (OpenAI plugins, LangChain tools, custom function calling) often lack rigorous security review. They operate with the permissions of the host application, creating privilege escalation vectors.
• RAG system poisoning — Retrieval-Augmented Generation systems combine retrieved documents with model generation. If retrieved documents are compromised (via shared knowledge bases, submitted content, or web sources), the model produces poisoned outputs.
• Workflow orchestration attacks — AI-powered workflow systems (n8n with AI nodes, Make.com AI actions) create attack surfaces through automated multi-step processes. A compromised AI step can corrupt an entire automation workflow.

Deployment & Infrastructure

• Exposed credentials — API keys, model tokens, and service credentials are frequently exposed in client-side code, log files, and error messages from AI-powered applications. AI coding assistants often suggest code that includes hardcoded credentials.
• GPU/compute resource abuse — GPUs running AI models are high-value targets for cryptojacking and resource exhaustion. Cloud AI services expose attack vectors through shared multi-tenant environments.
• Model serving vulnerabilities — Deployed models face traditional web vulnerabilities (injection, SSRF, RCE through model serving frameworks), plus model-specific issues like adversarial input, DoS through expensive inference attacks, and model data leakage.
• CI/CD pipeline attacks — ML pipelines (MLflow, Kubeflow, Airflow) are increasingly automated and AI-augmented. Compromised pipelines can inject poisoned models into production or intercept training artifacts.

Governance & Compliance

• AI audit deficiencies — Traditional security audits don't cover model-specific risks (bias, drift, memorization, robustness). No standardized AI security audit framework exists; organizations rely on ad-hoc evaluations.
• Model documentation gaps — Model Cards and datasheets are voluntary. Most deployed models lack documentation on training data, intended use, known limitations, and evaluation results. This makes risk assessment impossible for downstream consumers.
• Explainability failures — Black-box models make it impossible to explain decisions to regulators, auditors, or affected individuals. XAI tools (SHAP, LIME, Captum) provide post-hoc explanations but are not guarantees of model transparency.
• Regulatory compliance — The EU AI Act, US AI Executive Order, and sector-specific regulations (HIPAA, PCI-DSS for AI) create complex compliance requirements. Most organizations lack tools to systematically demonstrate compliance.

Supply Chain & Ecosystem

• Model marketplace risks — Hugging Face, ModelScope, and civit.ai host thousands of community models with no security vetting. "Trojan" models with hidden behavior activated by specific triggers have been demonstrated. Fine-tuning datasets can contain hidden instructions or backdoors.
• Package dependency attacks — AI/ML Python packages (transformers, langchain, llama-index) have been compromised via typosquatting and supply chain attacks. The AI package ecosystem moves fast and security review lags behind innovation.
• Training infrastructure compromise — Training clusters running distributed training (Ray, PyTorch DDP, DeepSpeed) expand the attack surface. Compromised training nodes can poison models or extract training data.
• Third-party AI service risk — Using OpenAI, Google, Anthropic, or other third-party APIs introduces data privacy, vendor lock-in, and service availability risk. Sensitive data sent to external APIs may be logged, used for training, or exposed in breaches.

Model Guardrails & Safety

• Guardrails AI (Anthemis) — Open-source framework for input/output validation, PII detection, toxicity filtering, and format enforcement. Integrates with LlamaIndex and LangChain. Provides structured output enforcement via regex, JSON schema, and pydantic. [Project]
• NVIDIA NeMo Guardrails — Open-source toolkit for adding configurable guardrails to conversational AI. Supports custom rules, keyword detection, and semantic moderation. Integrates with NeMo microservices. [GitHub]
• IBM watsonx.governance — Enterprise-grade AI governance platform with model monitoring, bias detection, and explainability. Provides automated documentation and regulatory compliance reporting. [Project]

LLM Gateways & API Management

• Portkey AI — AI gateway for routing, monitoring, and securing LLM API calls. Provides prompt templates, caching, failover, and real-time analytics. Supports 100+ LLM providers with unified API. [Project]
• Latency.ai — AI gateway focused on cost optimization, latency reduction, and quality scoring across multiple model providers. Includes prompt versioning and experiment tracking. [Project]
• Langfuse / Marblism — AI observability platforms for tracking LLM performance, costs, and quality. Provide tracing, evaluation, and anomaly detection for production AI systems. [Project]

Content Moderation & Filtering

• OpenAI Moderation API — Real-time content classification for harmful content categories. Widely deployed but has known limitations around jailbreak detection and multilingual content. [Docs]
• Azure AI Content Safety — Microsoft's enterprise content moderation service with text, image, and multimodal analysis. Integrates with Azure AI services and supports custom threat types. [Project]
• Sightengine / Perspective API — Third-party content moderation APIs with specialized capabilities (toxicity, hate speech, spam, explicit content). Used by enterprises building custom moderation pipelines. [Project]

Red Team & Testing

• Garak — Comprehensive LLM vulnerability scanner with 600+ probes covering 20+ vulnerability categories. Automated scanning, reporting, and continuous integration support. [GitHub]
• PyRIT (Microsoft) — Python Risk Identification Toolkit for generative AI. Supports automated red teaming, multi-turn attack generation, and integration with Azure AI services. [GitHub]
• HarmBench — Standardized benchmark for evaluating jailbreak resistance across models and attack methods. Provides reproducible evaluation methodology for safety research. [GitHub]

RAG Security & Knowledge Base Protection

• Inkeep AI — RAG-specific security platform with prompt injection detection, content filtering, and knowledge base poisoning protection. Monitors retrieval quality and detects adversarial queries. [Project]
• Deepset — Enterprise RAG platform with built-in security controls, including document classification, access control, and content validation. Supports hybrid search and semantic chunking. [Project]
• Vellum AI — Prompt engineering and evaluation platform with built-in safety testing, version control, and production monitoring for AI applications. [Project]

Threat Modeling & Risk Assessment

• MITRE ATLAS — Adversarial Threat Landscape for AI Systems. Knowledge base modeled on MITRE ATT&CK, covering AI-specific TTPs (techniques, tactics, and procedures) with mappings to the MITRE matrix. [atlas.mitre.org]
• NIST AI RMF — AI Risk Management Framework (1.0, 2023). Comprehensive guidance for managing AI risk across the lifecycle, including governance, mapping, measuring, and managing functions. [NIST]
• OWASP LLM Top 10 — Top 10 risk framework for LLM deployments. Updated 2023/2024, covers prompt injection, insecure output handling, training data poisoning, and more. Free and widely adopted. [OWASP]

Responsible AI Frameworks

Establish governance frameworks for responsible AI development.

• AI governance policies
• Accountability and transparency
• Risk management in AI
• Ethical AI principles
• Compliance frameworks (EU AI Act)
• Documentation and auditing

Model Explainability & Interpretability

Make AI model decisions transparent and understandable to stakeholders.

• Feature importance analysis
• SHAP values and attribution
• LIME for local explanations
• Attention visualization
• Decision tree interpretability
• Black-box model explanation

Bias Detection & Mitigation

Identify and reduce bias in AI systems for fair outcomes.

• Bias detection methodologies
• Fairness metrics
• Pre-processing debiasing
• In-processing fairness
• Post-processing techniques
• Fairness testing and auditing

Regulatory Compliance

Navigate regulatory requirements for AI systems and data.

• EU AI Act requirements
• GDPR compliance for AI
• Industry-specific regulations
• Data governance compliance
• Algorithm impact assessments
• Audit trails and reporting

Data Protection in ML

Protect personal data and sensitive information in ML systems.

• Differential privacy implementation
• Federated learning approaches
• Data anonymization techniques
• Privacy-preserving ML
• Secure multi-party computation
• Homomorphic encryption

Ethical Decision-Making

Guide ethical considerations in AI system design and deployment.

• Ethical review processes
• Stakeholder engagement
• Impact assessment
• Transparency requirements
• User consent and control
• Accountability mechanisms