Secure GenAI Adoption: Understanding LLM Threats
Large Language Models are the engine behind the generative AI revolution. They power chatbots, code assistants, document analysers, and decision support systems across every industry. But with this capability comes a new class of security threats that traditional application security doesn't fully address.
As a member of the OWASP Top 10 for LLM Applications working group, I've seen firsthand how quickly these threats evolve. Adopting GenAI securely starts with understanding them.
The OWASP Top 10 for LLM Applications
The OWASP Top 10 for LLM Applications is the most widely referenced framework for LLM-specific security risks. Start with these entries.
Prompt Injection
The most discussed LLM vulnerability, and the hardest to solve cleanly. Attackers craft inputs that override the model's system instructions and make it do something it was never designed to do. The injection can be direct, arriving in user input, or indirect, hidden in a document the model has been asked to process.
Defend it in layers: sanitise inputs, filter outputs, separate privileges between the model and any backend system it can reach, and treat everything the model produces as untrusted.
Sensitive Information Disclosure
LLMs expose sensitive data in ways that are easy to miss. Sometimes memorised training data surfaces in an answer. Other times a crafted prompt extracts the system instructions or whatever confidential context was loaded into the session.
The controls here are unglamorous. Classify data before it goes anywhere near the model, put proper access controls on retrieval-augmented generation (RAG) sources, and monitor outputs for the patterns you care about.
Supply Chain Vulnerabilities
The LLM supply chain is longer than teams tend to expect: pre-trained models, fine-tuning datasets, embedding models, vector databases, orchestration frameworks. Every one of those is an attack vector.
Assess your vendors, verify model provenance, scan dependencies, and keep every component patched. The discipline is the same as any other software supply chain. Only the components are unfamiliar.
Insecure Output Handling
Pass model output straight into another system, whether that means executing generated code, rendering HTML, or firing an API call, and the model becomes an injection vector against everything downstream.
Treat model output as untrusted input, because that is exactly what it is. Apply the same validation and sanitisation you would apply to anything a user typed in.
Excessive Agency
Connect a model to tools and APIs and it can take actions with real-world consequences. If it can send emails, query databases, or modify files, then whoever successfully manipulates its behaviour inherits those capabilities.
Least privilege applies here exactly as it does everywhere else. Scope every tool integration tightly, require human approval for high-impact actions, and put rate limiting and anomaly detection on anything the model initiates.
Building a Secure LLM Architecture
Individual controls only go so far. LLM security needs an architectural answer as well.
Defence in Depth
No single control stops every LLM attack. Layer them:
- Input filtering and prompt guards
- Model-level safety training and system prompts
- Output validation and monitoring
- Backend access controls and sandboxing
- Audit logging and anomaly detection
Separation of Concerns
Keep the LLM away from sensitive systems. Put intermediary services in between, so that something other than the model validates and authorises an action before it reaches production.
Monitoring and Observability
Log every interaction with the model: inputs, outputs, tool calls, errors. Then actually watch them for the signatures of prompt injection attempts, data exfiltration, and anomalous usage.
Regular Assessment
LLM security is a moving target, so a point-in-time assessment ages badly. Reassess on a regular cycle, covering:
- Automated prompt injection testing
- Manual red team exercises
- Review of RAG data access controls
- Supply chain dependency audits
- Compliance checks against applicable regulations
The Regulatory Dimension
The EU AI Act classifies AI systems by risk level and sets requirements accordingly. Systems in the high-risk category, which covers uses in critical infrastructure, healthcare, and financial services, carry the most stringent obligations for transparency, security, and human oversight.
If you are deploying LLMs into one of those sectors, the OWASP controls and the regulatory requirements need to be designed in together. Retrofitting either one is expensive.
Conclusion
The threats described here are new enough that most application security programmes miss them entirely, and they change faster than an annual review cycle can track. Build the security in while the architecture is still on a whiteboard. Bolting it on after a pilot has succeeded is a much harder and more expensive piece of work.
Start with the OWASP Top 10 for LLM Applications. It is practical rather than theoretical, and because it is community-driven it gets updated as the threats change rather than years after the fact.