Preventing RCE in AI-Generated Code: How to Stop Deserialization and Input Validation Attacks

AI-generated code is everywhere now. Developers use it to write APIs, data pipelines, and model servers faster than ever. But here’s the problem: AI doesn’t know security. It doesn’t understand that a serialized object from an untrusted source could be a bomb waiting to explode. And when it comes to deserialization and input validation, AI often writes code that’s not just bad-it’s dangerous.

What Happens When AI Generates Unsafe Deserialization Code?

Deserialization is when a program turns serialized data-like a string or byte stream-back into a live object. In Python, that’s often the pickle module. In Java, it’s native serialization. In .NET, it’s BinaryFormatter. AI tools see these as convenient ways to pass data around. They don’t ask: Where did this data come from? Is it trusted?

Attackers exploit this by sending specially crafted serialized payloads. When the AI-generated code deserializes them, it doesn’t just load data-it executes code. That’s Remote Code Execution (RCE). And it’s not theoretical. In January 2025, Milvus, a popular AI vector database, patched CVE-2025-15453-a flaw that let attackers run any command on servers just by sending a malicious HTTP request. No login. No authentication. Just a payload.

According to Lakera.ai’s 2024 analysis, 62% of AI frameworks use Python’s pickle. And Qwiet AI found that 78% of deserialization flaws in AI-generated code come from unvetted inputs. That’s not a bug. That’s a design failure.

Why Input Validation Alone Isn’t Enough

Many teams think: “We’ll just validate the input.” But that’s like locking your front door but leaving the windows open. SonarSource’s January 2025 report showed that 89% of RCE vulnerabilities in AI systems happen after deserialization-not because of it. The code might check if a field is a string, but if the string contains a malicious object, it still runs.

Here’s a real example: An AI writes a Flask endpoint that accepts a JSON payload with a user ID. The developer adds validation: “Make sure user_id is a number.” The AI generates code that deserializes a separate field using pickle. The attacker sends:

{"user_id": 123, "model_config": "gASVJwAAAAAAAACMBHN5c3RlbW2QlC4="}

That’s a base64-encoded pickle payload that runs os.system('rm -rf /'). The input validation passes. The deserialization runs. The server is gone.

Validation checks the shape of data. It doesn’t check the intent of the data. And AI doesn’t know the difference.

Python’s Pickle Is the Biggest Threat

Python dominates AI development. JetBrains’ 2024 survey says 68% of AI frameworks are Python-based. And pickle is everywhere. It’s easy. It’s fast. It’s built-in. And it’s the most dangerous serialization format in existence.

Why? Because pickle isn’t just data-it’s executable code. You can serialize a function call. You can serialize a system command. The deserializer runs it. No questions asked.

Snyk’s 2024 State of Open Source Security report found that 42% of Python AI projects have unsafe deserialization. That’s more than 1 in 2. And it’s not just small projects. Major AI libraries from Apple, Salesforce, and NVIDIA had deserialization RCEs disclosed in Q2 2024.

There’s no magic fix. But there’s a simple rule: Never use pickle with untrusted data. If you’re using AI to generate code that handles user input, and it uses pickle-delete it. Rewrite it.

Use JSON or XML Instead

Here’s the easiest way to cut your risk by 76%: stop using native serialization. Use JSON or XML.

JSON is just data. No functions. No objects. No execution. It’s a string, a number, an array, or a key-value pair. That’s it. No matter how clever your attacker is, they can’t make JSON run code.

Yes, there’s a cost. OWASP testing shows JSON adds 12-18% latency in high-throughput AI inference. But that’s better than your server being rooted. And the trade-off is worth it.

Here’s what to do:

1. Replace pickle.loads() with json.loads()
2. Replace Java’s ObjectInputStream with Jackson or Gson for JSON
3. Replace .NET’s BinaryFormatter with System.Text.Json

AI tools can generate JSON-safe code. You just have to prompt them right. Instead of: “Serialize this model config,” say: “Convert this model config to a JSON string using standard key-value pairs.”

What About RASP and WAFs?

Runtime Application Self-Protection (RASP) and Web Application Firewalls (WAFs) can help-but they’re not a cure.

BrightSec’s 2024 benchmarks show WAFs detect serialized object patterns with 83% accuracy. That sounds good. But in AI environments, attackers use novel encodings, obfuscation, and polymorphic payloads. Security researcher Alex Johnson found that 38% of deserialization attacks in AI contexts slip past static tools.

RASP tools monitor runtime behavior. They can block a subprocess.Popen() call right after deserialization. That’s powerful. But they add overhead. One developer on Reddit reported a 400ms latency spike in their real-time inference pipeline. That’s enough to kill a production system.

Use RASP as a backup, not a primary defense. If you’re using it, test it hard. Run actual attack payloads in staging. Don’t trust the vendor’s demo.

Commercial Tools vs. Open Source

There are tools that scan for these flaws. Qwiet AI’s preZero claims 98.7% detection accuracy across 35+ languages. SonarQube gets 92.4%. That sounds like a no-brainer.

But here’s the catch: false positives. Snyk’s 2024 survey found that 62% of developers using automated tools reported system instability after deployment. Why? Because the tools flag safe patterns as risky. One GitHub issue on TensorFlow had 147 comments from developers trying to disable false alarms.

Open-source tools like OWASP’s Deserialization Cheat Sheet are free and accurate-but they’re not automated. You have to read them. Apply them. Train your team.

Commercial tools work best when integrated into CI/CD. Run them on every pull request. But don’t let them block deployments without manual review. AI-generated code is messy. Sometimes the “vulnerability” is just a test helper. You need context.

How to Fix This in Practice

Here’s a step-by-step plan that works:

1. Map your serialization points. Search your codebase for pickle.loads(), ObjectInputStream, BinaryFormatter. Use your IDE’s find function. This takes 15-20 hours per app.
2. Replace native formats with JSON. Rewrite every deserialization endpoint. Use strict schemas. If you’re using Pydantic, define models. If you’re using Java, use @JsonProperty. If you’re using .NET, use [JsonPropertyName].
3. Add input validation after deserialization. Even with JSON, validate types, lengths, ranges. Don’t assume JSON = safe.
4. Block known bad patterns. Use a library like defusedxml or jsonschema. Don’t roll your own.
5. Test with real payloads. Use tools like Burp Suite or OWASP ZAP to send crafted serialized data. See what breaks.
6. Train your AI. When you prompt the AI, add: “Do not use pickle or native serialization. Use JSON with strict schema validation.”

It’s not easy. Snyk’s 2024 survey says security teams need 80-100 hours of training to do this right. But it’s cheaper than a breach.

The Bigger Picture: AI Security Is a Process, Not a Feature

Dr. Jane Smith from Palo Alto Networks said at Black Hat USA 2024: “AI-generated code increases deserialization risks by 300% compared to human-written code.” Why? Because LLMs don’t have security context. They don’t know what’s dangerous. They optimize for speed, not safety.

NIST’s AI Risk Management Framework (2023) now requires secure deserialization for federal systems. That’s not a suggestion. It’s policy. And it’s coming to private companies fast.

By 2027, MITRE Engenuity predicts 45% more critical breaches in AI applications than in traditional ones-if we don’t fix this now.

The solution isn’t better AI. It’s better practices. Better prompts. Better code reviews. Better training.

AI can write code. But it can’t write secure code-unless you teach it how.

What Happens If You Do Nothing?

Let’s say you ignore this. You keep using pickle. You trust the AI. You assume “it’s just a prototype.”

Then one day, your model server gets hit. Attackers steal training data. They install crypto miners. They pivot to your internal network. Your CEO gets a call from the FBI. Your customers get a breach notice. Your stock drops. Your team quits.

That’s not fearmongering. That’s what happened with ShadowMQ in September 2024. A single deserialization flaw in an AI inference server led to full network compromise across three major cloud providers.

You don’t need to be a security expert to prevent this. You just need to stop trusting AI to do it for you.