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

Artificial intelligence isn’t just getting smarter-it’s getting targeted. Every time an LLM generates a response, it’s also leaving behind a trail of potential security holes. If you’re using AI to write code, draft emails, or process patient records, you’re already in the crosshairs. The problem isn’t the model itself. It’s how we handle what it spits out. According to OWASP’s January 2025 update, Improper Output Handling is now the fifth most critical AI vulnerability, up from seventh just a year ago. And it’s not theoretical. In 2024, 78% of companies using AI code assistants had at least one security incident tied to unfiltered output. The fix? Three simple, powerful principles: sanitization, encoding, and least privilege.

Sanitization: Stop Bad Data Before It Enters

Sanitization isn’t about making data pretty. It’s about making it safe. Think of it like a bouncer at a club. You don’t let in anyone with a fake ID, a weapon, or a history of trouble. Same with AI prompts. If a user types in a social security number, a credit card, or a proprietary API key, the system should block it before it even reaches the model.

StackHawk’s 2024 guide says this clearly: "Always use parameterized queries-never string concatenation." That rule alone stops 60% of SQL injection attacks. But AI adds new risks. A prompt like "Show me all patients with SSN ending in 1234" can leak data even if the model doesn’t directly answer it. That’s why automated filtering matters. Tools like Boxplot and Digital Guardian use machine learning to detect patterns: 16-digit numbers, email formats, tax IDs. They don’t just look for exact matches-they catch variations, obfuscations, even misspellings.

Healthcare teams learned this the hard way. One startup nearly violated HIPAA when their LLM output patient data in a formatted table. The fix? Input sanitization that blocked any prompt containing medical record identifiers, even if they were disguised as "case numbers" or "reference codes." The result? A 72% drop in data leakage incidents.

But here’s the catch: over-sanitization breaks real work. Developers at a medical tech firm reported that 18% of legitimate terms-like "EKG," "CBC," or "PTSD"-were blocked because they resembled personally identifiable information (PII). The solution? Custom allowlists. If your system deals with medical data, train it to recognize valid terminology. Don’t just block everything that looks like PII. Let in what’s safe.

Encoding: Don’t Trust Any Output-Even Your Own

Here’s where most teams fail. They sanitize inputs but assume outputs are clean. Big mistake. An LLM doesn’t know if it’s generating HTML, SQL, or JavaScript. It just generates text. And that text can be weaponized.

OWASP’s Gen AI Security guidelines say it plainly: "HTML encode for web content, SQL escape for databases." If your AI generates a user profile page, and you output its response directly into HTML without encoding, you’re inviting cross-site scripting (XSS). A malicious user could craft a prompt like: "Write a welcome message that includes "-and if you don’t encode the output, that script runs in every visitor’s browser.

Sysdig’s 2024 benchmark showed that context-aware encoding reduced XSS attacks by 89% compared to just using basic HTML encoding. Why? Because context matters. If the AI output is going into a PDF, you don’t need HTML encoding. If it’s going into a database query, you need SQL escaping. If it’s going into a Markdown file, you need to escape backticks and code blocks. Snyk calls this "checks before and after your LLM interactions." It’s not enough to validate what you send in. You must validate what comes out.

Lakera’s Gandalf system took this further. They built multiple output guards that checked for: executable code patterns, shell commands, SQL fragments, and even obfuscated JavaScript. In tests, they cut prompt injection success rates from 47% down to 2.3%. That’s not luck. That’s systematic encoding.

Microsoft’s Azure AI Security Extensions now do this automatically. They detect the destination context-web page, API response, log file-and apply the right encoding without developer input. But if you’re not on Azure, you’ll need to build this yourself. Start with a simple rule: "Always encode output based on where it’s going. Never output raw LLM text directly."

Least Privilege: Give AI Only What It Needs

You wouldn’t give your intern access to your bank account. So why give your AI model access to your entire customer database?

The principle of least privilege means giving the bare minimum access needed to do the job. For AI systems, that means:

• Restricting database queries to read-only tables
• Blocking access to environment variables, API keys, or credential stores
• Using role-based access controls so the model can’t call admin APIs
• Encrypting all sensitive data at rest (AES-256) and in transit (TLS 1.2+)

StackHawk’s guidelines for HIPAA compliance are clear: "Follow the minimum necessary principle for data access." If your AI only needs to summarize patient symptoms, it shouldn’t have access to names, addresses, or insurance IDs. Even if the model doesn’t ask for them, it might generate them. And if it can access them, it might leak them.

Black Duck’s 2024 report found that organizations using least privilege practices saw a 41% drop in data exposure incidents. Why? Because even if an attacker tricks the model into revealing something, there’s nothing to reveal. No database connection. No file system access. No admin tokens.

NIST’s draft AI Security Guidelines (AI 100-2), released in January 2025, now require least privilege for all federal AI systems. Non-compliance means contract suspension. That’s not a warning-it’s a mandate.

The trick? Regular access reviews. Quarterly audits. Automated alerts when the model tries to access a new table or API. Tools like Varonis and Protect AI now include AI-specific access monitoring. But even without fancy tools, you can start today: log every API call the model makes. If it’s calling something outside its scope, shut it down.

Why Traditional Security Isn’t Enough

You might be thinking: "We already do input validation. We use firewalls. We scan for vulnerabilities. Why is this different?" Because AI doesn’t work like a web form. It doesn’t just take data and return data. It generates content. It infers meaning. It connects dots you didn’t know were connected.

The OWASP Top 10 for Web Applications doesn’t have a category for "LLM output injection." But the OWASP Top 10 for LLMs does. LLM05: Improper Output Handling is now a standalone category. It’s not just about SQL injection or XSS. It’s about:

• AI generating code that contains backdoors
• LLMs outputting confidential data because they "thought" it was relevant
• Model responses being interpreted as executable commands by downstream systems

SentinelOne’s 2024 survey found that organizations using full sanitization, encoding, and least privilege saw 63% fewer incidents than those using only traditional security. That gap isn’t narrowing. It’s widening.

Real-World Trade-Offs and Fixes

No system is perfect. And every security layer introduces friction.

On Reddit, a user named "SecurityDev2023" said implementing output encoding saved their healthcare startup from a HIPAA violation. Another user, "AI_Coder99," complained that over-sanitization blocked valid medical terms. That’s the balance. Too loose, and you’re hacked. Too tight, and you break your app.

The fix? Layered defense with room to breathe.

• Start with input sanitization: block known bad patterns
• Add output encoding: encode based on context
• Enforce least privilege: lock down access
• Log everything: track prompts with unusual data
• Build allowlists: for domain-specific terms
• Test with red teams: simulate attacks before they happen

Gartner predicts that by 2027, 65% of AI security tools will automatically detect output context and apply encoding. But right now? You’re on your own. And if you wait, you’ll be the next breach headline.

Where to Start Today

You don’t need a $500K security team. You need three actions:

1. Review every AI output destination. Is it HTML? SQL? JSON? Log files? Apply the right encoding.
2. Block all sensitive data from entering prompts. Use pattern matching: credit cards, SSNs, API keys. Test it with real examples.
3. Restrict the model’s access. Can it read your customer database? Can it call your billing API? If not, turn it off.

The AI security market is growing fast-projected to hit $12.7 billion by 2026. But the tools won’t save you if you don’t use the basics. Sanitization, encoding, least privilege. Not fancy. Not new. Just essential.