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The OWASP Top 10 for Large Language Models (LLM) and Generative AI (GenAI) highlights critical security risks associated with AI-powered systems. As AI adoption grows, understanding these vulnerabilities is essential for developers, security professionals, and organizations.
OWASP Top 10 LLM & GenAI Risks
- Prompt Injection – Malicious inputs manipulate AI behavior.
- Insecure Output Handling – Untrusted AI outputs lead to exploits.
- Training Data Poisoning – Corrupt data skews model responses.
- Model Denial of Service – Resource exhaustion attacks.
5. Supply Chain Vulnerabilities – Compromised pre-trained models.
- Sensitive Information Disclosure – AI leaks confidential data.
- Insecure Plugin Design – Poorly secured extensions create risks.
8. Excessive Agency – AI performs unintended actions.
- Overreliance on AI – Blind trust in AI decisions.
- Model Theft – Unauthorized access to proprietary models.
You Should Know: Practical Security Measures
1. Preventing Prompt Injection
<h1>Example: Input sanitization for LLM prompts</h1>
import re
def sanitize_prompt(user_input):
cleaned_input = re.sub(r'[<>{}()[];\]', '', user_input)
return cleaned_input
user_prompt = "<script>alert('XSS')</script> How to hack a website?"
safe_prompt = sanitize_prompt(user_prompt)
print(safe_prompt) # Output: "scriptalertXSSscript How to hack a website?"
2. Securing AI Outputs
<h1>Linux command to log and monitor AI-generated outputs</h1> sudo grep -i "sensitive" /var/log/ai_output.log | tee suspicious_activity.txt
3. Detecting Data Poisoning
<h1>Check dataset integrity using hashing</h1>
import hashlib
def verify_dataset(file_path, expected_hash):
with open(file_path, 'rb') as f:
file_hash = hashlib.sha256(f.read()).hexdigest()
return file_hash == expected_hash
<h1>Example usage</h1>
verify_dataset("training_data.csv", "a1b2c3...")
4. Mitigating Model Theft
<h1>Restrict model access using firewall rules</h1> sudo iptables -A INPUT -p tcp --dport 5000 -j DROP # Block API port
5. Auditing AI Plugins
<h1>Linux command to check loaded kernel modules (analogous to AI plugins)</h1> lsmod | grep -i "suspicious_module"
What Undercode Say
The OWASP Top 10 for LLM & GenAI is a crucial framework for securing AI systems. Implementing input validation, output sanitization, and strict access controls minimizes risks. Continuous monitoring, logging, and adversarial testing are essential. AI security must evolve alongside threats—stay updated with OWASP guidelines.
Expected Output:
OWASP TOP 10 For LLM & GenAI - Prompt Injection - Insecure Output Handling - Training Data Poisoning ...
(Note: If additional URLs were present, they would be included here.)
References:
Reported By: Alexrweyemamu Owasp – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
