Securing the AI Frontier
Secure Today. Defend Tomorrow.
Comprehensive analysis of AI security risks, model vulnerabilities, and defensive strategies. From autonomous agents to model supply chains — we track the evolving threat landscape.
AI Security Landscape
Key security domains every organisation deploying AI systems must understand and address.
AI Agents
Autonomous AI agents introduce unique security challenges including tool misuse, credential theft, and unintended side effects during multi-step reasoning.
- Agent-to-agent communication must be authenticated and encrypted.
- Capability boundaries should be enforced to prevent privilege escalation.
- Human-in-the-loop verification for high-impact actions reduces autonomous risk.
- Session isolation prevents cross-agent contamination.
Model Context Protocol (MCP)
MCP standardises how LLMs interact with external tools and data sources, introducing new attack surfaces through context injection and tool routing.
- Context injection attacks can manipulate model behaviour through crafted inputs.
- Tool discovery and routing must be access-controlled to prevent unauthorised tool use.
- Rate limiting and input validation on MCP endpoints prevent resource exhaustion.
- Audit logging of all MCP interactions enables forensic analysis.
Guardrails & Safety Controls
Guardrails enforce model behaviour boundaries, preventing harmful outputs, policy violations, and data leakage through layered validation.
- Input guardrails filter prompt injection and jailbreak attempts before they reach the model.
- Output guardrails enforce content policies and prevent sensitive data disclosure.
- Context-aware guardrails adapt restrictions based on user role and conversation context.
- Continuous monitoring and updating of guardrail rules addresses emerging threat patterns.
Skills & Plugins
Third-party skills and plugins extend model capabilities but introduce supply chain risks, permission escalation, and data exfiltration vectors.
- Plugin manifest verification ensures only authorised skills are loaded.
- Permission scoping restricts skills to the minimum required access level.
- Sandboxed execution environments isolate plugin runtime from core systems.
- Vendor security assessment and regular updates mitigate supply chain risks.
Prompt Injection Defense
Prompt injection remains the most critical LLM attack vector, requiring multi-layered defences at the input, context, and output stages.
- Structural separation of system prompts from user input prevents role confusion.
- Input sanitisation strips control characters and anomalous patterns.
- Output verification confirms the model response adheres to the intended task scope.
- Rate limiting on sensitive operations reduces blast radius of successful injections.
Data Privacy in AI
AI systems process vast amounts of data, requiring robust privacy controls including data minimisation, differential privacy, and secure inference.
- Data minimisation ensures only necessary information is sent to the model.
- Differential privacy techniques protect individual records in training data.
- Secure enclave inference prevents data exposure during model processing.
- Prompt sanitisation redacts PII before it reaches the LLM provider.
AI Supply Chain Security
The AI supply chain spans model weights, training data, third-party libraries, and deployment infrastructure, each link presenting unique risks.
- Model provenance verification ensures weights haven't been tampered with.
- Training data lineage tracking identifies potential data poisoning vectors.
- Dependency scanning for AI frameworks catches known vulnerabilities.
- Reproducible builds enable verification of model integrity from source.
LLM Model Card Library
Security-focused model cards from major LLM providers. Browse, compare, and download model card data with security and risk analysis.
Data Freshness Notice
Model card data is synced daily from Hugging Face, with Anthropic and Google model details sourced directly from their official vendor pages. Covers the last 12 months only. Model cards older than 12 months are automatically removed from our database.
Gemini 1.5 Pro
Mid-size multimodal model with up to 1M token context window, strong reasoning, and expert-level capabilities.
Gemini 2.0 Flash
High-speed, cost-efficient model for large-scale deployments with multimodal understanding and tool use.
Gemini 2.5 Pro
Google's most capable AI model with advanced reasoning, long context, multimodal understanding, and agentic capabilities.
Gemma 4
Google's lightweight open model family with strong reasoning, coding, and multilingual capabilities. Available in multiple sizes.
Claude 2
Predecessor model with strong natural language understanding, coding, and safety alignment.
Stay Updated on New Model Cards
Subscribe to receive notifications when new model cards are added or updated.