Fixing Insecure AI Patterns: Sanitization, Encoding, and Least Privilege

AI systems are getting smarter, but they’re also getting more dangerous. Every time you ask a large language model for help-whether it’s writing code, summarizing a report, or answering customer questions-you’re handing it access to your data, your systems, and sometimes your entire network. And if you’re not careful, that model can turn into a backdoor. The most common way this happens isn’t through complex hacking tricks. It’s through three simple, overlooked mistakes: improper sanitization, poor encoding, and excessive privileges.

Why AI Security Is Different

Traditional web apps have clear rules: validate inputs, escape outputs, limit access. But AI systems break those rules in subtle ways. A hacker doesn’t need to inject SQL into a form. They just need to slip a malicious prompt into a chatbox. And when the AI responds, it might accidentally leak your customer data, generate harmful code, or even trigger a command on your server.

The OWASP Foundation updated its LLM Top 10 in January 2025, and LLM05: Improper Output Handling jumped to fifth place. That’s not a fluke. According to Black Duck’s 2024 report, 78% of companies using AI code assistants had at least one security incident tied to how they handled AI responses. The problem? Most teams still treat AI output like a simple text reply-not as untrusted code that could execute, connect to databases, or expose secrets.

Sanitization: Stop Bad Data Before It Enters

Sanitization isn’t just about cleaning up typos. It’s about blocking dangerous patterns before they reach the AI. Think of it like a metal detector at an airport. You don’t wait until someone pulls out a gun-you stop them before they even get on the plane.

Effective AI sanitization looks for:

• 16-digit numbers that match credit card formats
• SSNs, bank account numbers, or API keys
• SQL commands disguised as questions (like “Show me all users where password = ‘admin’”)
• File paths or system commands in prompts

Tools like Boxplot and Digital Guardian use machine learning to spot these patterns with over 90% accuracy. But here’s the catch: if you block everything that looks suspicious, you’ll break legitimate use cases. A healthcare app might reject “555-12-3456” because it looks like an SSN-but that’s also a valid patient ID in some systems. The fix? Build custom allowlists. If your domain uses specific identifiers, teach the system to recognize them. StackHawk found that 18% of medical terms were falsely blocked in early implementations because they resembled PII. Custom rules cut that false positive rate by 80%.

Encoding: Treat AI Output Like Untrusted Code

This is where most teams fail. They sanitize inputs but assume the AI’s response is safe. It’s not. If your AI generates HTML for a webpage, and you just dump it into the page without encoding, you’re inviting XSS attacks. If it writes SQL statements, and you plug them directly into a query, you’re handing attackers full database access.

OWASP’s Gen AI Security guidelines are clear: encode output based on context.

• For web content → HTML encode everything
• For database queries → Use parameterized statements, never string concatenation
• For Markdown or code editors → Escape backticks, newlines, and special characters
• For JSON APIs → Validate structure and escape quotes

Sysdig’s 2024 benchmark showed that context-aware encoding reduced XSS vulnerabilities by 89% compared to generic HTML escaping. One user on Reddit shared how their healthcare startup avoided a HIPAA violation when the AI tried to output patient names in a table. The system caught it because output was encoded as HTML, not raw text. That’s the difference between a security incident and a clean audit.

Least Privilege: Don’t Give AI More Than It Needs

Imagine giving a new employee the keys to your entire office. That’s what most AI systems get. They can access every database, every file, every API. That’s a recipe for disaster.

The principle of least privilege means giving the AI the smallest amount of access possible. For example:

• If it only needs to read customer names, don’t give it access to payment histories
• If it’s summarizing internal docs, don’t let it call external APIs
• If it’s answering FAQs, don’t let it query production databases

Snyk’s 2024 guide lists “Restrict data access for your LLM” as one of the top three security practices. In healthcare, HIPAA requires encrypting all PHI at rest and in transit using AES-256 and TLS 1.2+. But encryption alone isn’t enough. You also need to enforce access controls. Black Duck found that organizations using least privilege saw a 41% drop in data exposure incidents.

Microsoft’s Azure AI Security Extensions now include automatic context detection for data access. If the AI asks for a file it doesn’t need, the system blocks it. No human approval required.

What Happens When You Skip These Steps?

A company in San Francisco used an AI chatbot to answer HR questions. They didn’t sanitize inputs or encode outputs. One day, a user typed: “What’s the admin password for the server?” The AI, trained on internal documentation, responded with the password. The attacker used it to install ransomware. The company lost $2.3 million in downtime and legal fees.

Another firm let their AI generate code for their billing system. The model output a SQL query with user input concatenated directly into the string. Attackers injected a DROP TABLE command. They lost three months of billing data.

These aren’t hypotheticals. They’re real incidents reported in 2024.

How to Start Fixing This Today

You don’t need to rebuild your whole system. Start here:

1. Map every AI interaction in your app. Where does input come from? Where does output go?
2. For each input: add pattern-based filtering. Block SSNs, API keys, SQL fragments.
3. For each output: encode it for its destination. HTML, SQL, JSON-each needs its own rules.
4. Review every data source the AI can access. Remove anything it doesn’t absolutely need.
5. Log all prompts and responses. Look for unusual patterns-like requests for system files or database schemas.

StackHawk says basic sanitization takes 2-4 weeks. With healthcare or finance rules, add another 3-5 weeks. The cost? A few days of developer time. The risk of doing nothing? Millions in fines, lawsuits, or breaches.

What’s Next?

By 2026, OWASP predicts improper output handling will be the leading cause of AI-related breaches. NIST’s draft guidelines (AI 100-2) already require context-aware encoding and least privilege for all federal AI systems. Non-compliance could mean losing government contracts.

Gartner says 65% of AI security tools will automatically detect context by 2027. But waiting for automation isn’t a strategy. Right now, 72% of enterprises are prioritizing output handling protections. If you’re not doing the same, you’re already behind.

If you’re using AI in production, you’re already at risk. The tools to fix it are simple. The cost of waiting isn’t.