Introduction
😤 I cussed myself out afterward, but I needed to run some urgent data analysis on a batch of sensitive legal documents—and, well, I was too pressed for time to figure out a proper sanitization process. In a moment of desperation, I uploaded my own documents to an LLM, fully aware that my PII was in the mix. Needless to say, it was a wake-up call about the real-world risks of handling sensitive data with LLMs.
🚀 Large Language Models (LLMs) have revolutionized our ability to process and understand natural language, but their widespread adoption comes with a host of security challenges. The 2025 OWASP Top 10 for LLM Applications is a timely guide that highlights the most critical vulnerabilities in this space, serving as an essential resource for developers and organizations looking to secure their AI systems. In this post, we’ll explore these risks and discuss mitigation strategies, including robust client-side sanitization, strong data classification, and privacy-preserving techniques like pseudonymization.
The 2025 OWASP Top 10 for LLM Applications
- Prompt Injection (LLM01:2025)
Attackers craft inputs that manipulate LLMs into performing unintended actions or generating harmful outputs.
Example: Embedding malicious instructions within user inputs to alter the LLM’s behavior. - Sensitive Information Disclosure (LLM02:2025)
LLMs inadvertently expose confidential data from their training sets or user interactions.
Example: An LLM revealing personal data embedded in its training content. - Supply Chain Vulnerabilities (LLM03:2025)
Risks arise from compromised third-party components, such as pre-trained models or datasets, integrated into LLM applications.
Example: A tampered pre-trained model introducing backdoors into the application. - Insecure Output Handling (LLM04:2025)
LLMs may generate responses that include harmful content, leading to security risks or the spread of misinformation.
Example: An LLM producing unfiltered outputs that could be exploited by malicious actors. - Excessive Agency (LLM05:2025)
Over-reliance on LLMs for autonomous decision-making without sufficient human oversight can result in automation failures.
Example: Allowing an LLM to execute critical financial transactions without manual review. - Misinformation and Overreliance (LLM06:2025)
LLMs may disseminate inaccurate information that, if trusted without verification, can have significant consequences.
Example: Incorrect medical advice provided by an LLM leading to unsafe practices. - System Prompt Leakage (LLM07:2025)
LLMs might inadvertently reveal their internal configuration or system prompts, providing attackers with insights to craft targeted exploits.
Example: Disclosing system instructions in responses that can be reverse-engineered. - Vector and Embedding Weaknesses (LLM08:2025)
Vulnerabilities in the data representation methods (embeddings) used by LLMs can be exploited to inject malicious information.
Example: Manipulating embedding techniques to cause unintended model behavior. - Denial of Service via Resource Exhaustion (LLM09:2025)
Attackers can overwhelm LLMs with complex or excessive queries, leading to service disruptions and downtime.
Example: Flooding an LLM with large inputs that exhaust its computational resources. - Bias and Fairness Issues (LLM10:2025)
LLMs may generate outputs that reflect inherent biases from their training data, leading to discriminatory or unfair results.
Example: Consistently skewed outputs that favor or disfavor specific groups.
Mitigation Strategies for LLM Users
When working with LLMs, data security starts before you ever hit “submit”. A few key strategies can significantly reduce risk:
1. Input Sanitization & Client-Side Protection
Most users—myself included—interface with LLMs through a chatbot UI. That means the biggest security risk is feeding them sensitive data without realizing it. A simple, effective solution? Sanitize the data before it ever leaves your device.
Client-side sanitization ensures:
✅ No raw sensitive data is transmitted
✅ PII is masked before submission
✅ Data is still useful, but anonymized
A great example of this in action is PPI Shield Bro, an open-source tool that automatically redacts PII, applies CUI markup, and generates sanitized Markdown—all within the browser. No server processing. No risk of exposure.
For a step-by-step guide on implementing AI-driven sanitization in the browser, check out:
🔗 Automatically Sanitize Data in the User’s Browser with AI
2. Strong Data Classification: Know What You’re Dealing With
Not all data is created equal. Before sending anything to an LLM, organizations should have clear classification rules that determine:
🔹 What data can be freely shared?
🔹 What requires anonymization or pseudonymization?
🔹 What should never touch an AI model?
This prevents accidental exposure of trade secrets, sensitive internal documents, or personal information—something I clearly failed at in my moment of desperation.
3. Secure Output Handling: Trust, but Verify
Just because an LLM generates text doesn’t mean it’s safe. Developers should:
✅ Implement validation layers before using LLM output in automated systems.
✅ Sanitize AI-generated responses to prevent XSS or injection attacks.
✅ Ensure responses don’t leak unintended sensitive data.
4. Human Oversight & Access Controls
AI tools should augment decision-making, not replace it.
- Enforce role-based access controls for AI-assisted tools.
- Keep audit logs of LLM interactions.
- Require human review for high-stakes AI outputs.
Poor Operational Security
LLMs are incredibly powerful, but bad security practices will burn you fast. I learned that the hard way when I accidentally handed over my own legal documents without a second thought.
Developers need to protect users from themselves. If you’re building with AI, you owe it to your users to:
🔹 **Sanitize inputs
Conclusion
As LLMs become increasingly integrated into critical applications, understanding and mitigating their security risks is not optional—it’s essential. The 2025 OWASP Top 10 for LLM Applications serves as a vital resource for identifying vulnerabilities and implementing effective countermeasures.
By combining rigorous input sanitization (ideally performed client-side), strong data classification, and advanced privacy-preserving techniques like homomorphic encryption, developers can build robust defenses that protect sensitive data throughout the AI processing pipeline. Embracing these strategies not only enhances security but also builds trust with end-users—ensuring you won’t end up, like I once did, frantically uploading your own sensitive documents because there was no time for proper sanitization.
