ATLAN TEAM
LLM features change the threat landscape
AI assistants, chatbots, and automated workflows create new paths into systems that traditional web security tools were never designed to test. Every AI-connected interface should be treated as an exposed service.
How the attack surface expands
- Prompt injection: Adversaries can embed instructions in user input or documents to override system intent.
- Integration abuse: LLMs that call tools or APIs can be manipulated into unauthorized actions.
- Data leakage: Poor output controls can expose sensitive data from knowledge bases or logs.
- Automation by attackers: AI also accelerates adversary discovery and exploitation cycles.
What security leaders should do
Extend threat models to include AI interactions, test prompt injection and tool abuse, and monitor LLM outputs with validation layers. Treat AI systems as a blend of software and ML, requiring both application security and model-specific controls.
As AI becomes a primary interface, LLM-focused testing is now a standard part of web security.
RAG pipelines and tool integrations change the risk model
LLM-enabled applications often connect to internal knowledge bases and toolchains. This means attackers can target the model to manipulate downstream systems, exfiltrate data, or escalate privileges through chained prompts. The model becomes a high-privilege interface that must be tested like any API gateway.
Security leaders should ensure AI features are scoped in line with the full web stack. That includes validation of how prompts, context, and tool calls are logged, monitored, and controlled.
Defensive controls that matter
- Input validation and prompt hardening: Reduce prompt injection risk and enforce robust system prompts.
- Output governance: Add response validation and sensitive data filtering before responses reach users.
- Tool access controls: Apply least privilege to model-triggered actions and log every call.
To assess these risks systematically, start with LLM Penetration Testing and view our methodology here. If your AI features sit inside a broader application, you can pair this with Web Application Testing for end-to-end assurance.
LLM security is now a core part of application security, not a specialist edge case.