As Large Language Models (LLMs) become a core part of modern applications—from automated customer support to intelligent data extraction—new security challenges are emerging. Just like traditional web applications evolved with the rise of OWASP's security standards, today's AI-driven systems are facing new attack surfaces, new vulnerabilities, and new risks.
To help organizations build safer AI systems, OWASP introduced the OWASP Top 10 for Large Language Models, a comprehensive guide outlining the most critical vulnerabilities impacting LLM-integrated applications.
Below is a practical, business-focused breakdown of what each risk means, how attackers exploit it, and how companies can mitigate it.
1. Prompt Injection
Prompt injection occurs when attackers manipulate model inputs to force the LLM into executing harmful or unintended actions.
Example: A user inputs text that overrides system instructions and extracts confidential data.
How to mitigate:
- Layered input validation and sanitization
- Strict system prompts with role separation
- Guardrail models or content filters
2. Insecure Output Handling
LLMs generate text, code, or instructions that may be executed by downstream systems. If not validated, this can lead to severe security issues.
Example: An LLM outputs a SQL query that is executed directly by your backend, unintentionally exposing sensitive data.
How to mitigate:
- Never directly execute model-generated content
- Add review, approval, or safety filters before execution
3. Training Data Poisoning
Attackers may insert malicious data into the model's training sources, skewing the model's outputs or exposing vulnerabilities.
Example: Public datasets get contaminated with instructions that bias or mislead the model.
How to mitigate:
- Use curated and trusted datasets
- Validate data pipelines for integrity
- Monitor for abnormal model behaviors
4. Model Theft
Since LLMs represent significant intellectual property, attackers may attempt to extract or replicate the model architecture, weights, or behavior.
Example: Sending large volumes of queries to reverse-engineer the model.
How to mitigate:
- Rate limiting and anomaly detection
- Watermarking or output fingerprinting
- API authentication and access control
5. Sensitive Data Leakage
LLMs trained on unfiltered data can unintentionally reveal private or confidential information.
Example: Model recalls PII, internal notes, or proprietary documents during conversations.
How to mitigate:
- Redact sensitive data before training
- Implement DLP (Data Loss Prevention) checks on responses
- Use encryption and privacy-preserving training methods
6. Excessive Agency
LLMs integrated with tools (like databases, APIs, email systems, automation scripts) can cause unintended real-world actions if not properly restricted.
Example: A model connected to an automation system accidentally deletes records when responding to a request.
How to mitigate:
- Use permission-limited tool interfaces
- Implement human-in-the-loop approval
- Add strict action validation and safety checks
7. Overreliance on Model Content
LLMs sometimes generate incorrect, misleading, or hallucinated information. Relying blindly on these outputs introduces serious risks.
Example: A model provides faulty legal or medical advice.
How to mitigate:
- Add fact-checking pipelines
- Implement uncertainty scoring
- Make it clear when outputs are recommendations vs. authoritative answers
8. Model Denial of Service (DoS)
Attackers may overload LLM systems with high-cost prompts or large inputs to degrade performance or increase operational costs.
Example: Sending extremely long prompts or repeated complex queries.
How to mitigate:
- Rate limiting
- Input size restrictions
- Monitoring usage spikes and anomaly detection
9. Supply Chain Vulnerabilities
LLMs rely heavily on third-party libraries, open-source models, embedding systems, and vector databases. Each component introduces risk.
Example: A compromised dependency in your AI pipeline leads to a system-wide breach.
How to mitigate:
- Use vetted and up-to-date libraries
- Maintain SBOMs (Software Bill of Materials)
- Scan for vulnerabilities continuously
10. Unauthorized Code Execution
LLMs capable of generating or running code (e.g., agentic systems, RPA, or autonomous copilots) may inadvertently produce harmful scripts.
Example: An LLM produces a shell command that, if executed blindly, deletes system files.
How to mitigate:
- Strict sandboxing of all execution environments
- Disallow direct execution of user-provided or model-generated code
- Implement output filtering and safety reviews
Why This Matters for Modern Businesses
LLMs introduce enormous opportunities—but also unfamiliar attack surfaces. For companies integrating AI into their workflows, the OWASP Top 10 for LLMs provides a foundational security framework that helps teams:
- Reduce risk
- Increase reliability and trust
- Protect sensitive data
- Avoid financial and operational damage
- Ensure compliance with regulatory requirements (GDPR, HIPAA, SOC2, etc.)
Security must evolve with technology—and as AI becomes central to modern systems, organizations need to adapt.
How DigitalCoding Helps Businesses Build Secure AI Systems
At DigitalCoding, we specialize in building secure, scalable, production-ready AI and cloud solutions. Our approach includes:
- Secure prompt engineering
- Model access controls and rate limiting
- Data sanitation + PII filtering
- Training pipeline hardening
- Vector database security
- RAG (Retrieval Augmented Generation) safety layers
- Audit logging and monitoring
If you're integrating LLMs into your application, we can help ensure your architecture is fast, cost-efficient, and resilient against modern AI threats.
Ready to secure your AI systems? Contact us to learn how DigitalCoding can help protect your LLM-powered applications.
