ATLAN TEAM
LLM attack vectors are now a standard risk
LLM applications introduce new failure modes that CISOs must understand. The most common threats focus on manipulating prompts, abusing tools, and extracting sensitive data.
Core attack vectors
- Prompt injection: Hidden or direct instructions force the model to ignore system constraints.
- Insecure output handling: The model is coerced into disclosing sensitive data or unsafe actions.
- Tool and API exploitation: Attackers chain prompts to trigger unauthorized function calls.
- Training data poisoning: Malicious data influences model behavior or responses.
- Model theft and over-reliance: Excessive querying or blind trust in outputs creates exposure.
Defensive playbook
- Sanitize inputs and detect suspicious instruction patterns.
- Validate outputs before they reach users or downstream systems.
- Constrain tool access with least privilege and explicit allowlists.
- Monitor for abnormal query patterns and enforce rate limits.
- Maintain an inventory of models, prompts, and integrations.
Regular LLM red teaming brings these risks under control and ensures AI initiatives do not become the weakest link.
Why these vectors persist
LLM systems are often deployed quickly, and default safety controls are rarely sufficient. Attackers exploit ambiguity in prompts, over-permissive tool access, and gaps between the model and downstream systems. That combination creates repeatable, high-impact attack paths.
Defense in depth for LLM systems
- Input controls: Normalize inputs and detect prompt injection patterns before they reach the model.
- Output validation: Scan responses for sensitive data, policy violations, and malicious content.
- Tool constraints: Enforce explicit allowlists and restrict model permissions to minimum required.
- Monitoring and rate limits: Detect abusive query patterns and block automated probing.
These controls are most effective when tested by specialists. Start with LLM Penetration Testing and view our methodology here for how we test these vectors.
If your AI features sit inside a wider application, you can pair this with Web Application Testing to validate end-to-end security.