LLM Security Agent

Specialized agent for detecting security vulnerabilities in Large Language Model (LLM) applications and AI systems.

Overview

The LLM Security Agent analyzes applications that integrate Large Language Models (like GPT, Claude, or other AI systems) for security vulnerabilities specific to AI/ML systems. As LLM applications become more prevalent, new security challenges emerge that traditional security tools don’t address.

Agent Type: Code & Application Security
Complexity: Medium
Scan Duration: 10-30 seconds
Best For: AI/ML applications, LLM integrations, chatbots, AI agents

What It Detects

1. Prompt Injection Vulnerabilities

Detects code vulnerable to prompt injection attacks where malicious user input can manipulate LLM behavior.

Example:


# VULNERABLE
user_input = request.json['message']
prompt = f"You are a helpful assistant. User says: {user_input}"
response = llm.complete(prompt)
 
# SECURE
user_input = sanitize_input(request.json['message'])
prompt = format_prompt(user_input, role="user")
response = llm.complete(prompt)

2. Insecure LLM Output Handling

Identifies cases where LLM outputs are used without proper validation.

Example:


# VULNERABLE - executing LLM-generated code
code = llm.generate_code(user_request)
exec(code)  # Dangerous!
 
# SECURE
code = llm.generate_code(user_request)
if validate_generated_code(code):
    exec(code, safe_globals, safe_locals)

3. API Key Exposure

Finds hardcoded or improperly stored LLM API keys.

Example:


# VULNERABLE
openai.api_key = "sk-proj-abc123..."  # Hardcoded!
 
# SECURE
openai.api_key = os.getenv("OPENAI_API_KEY")

4. Excessive LLM Permissions

Detects LLM agents with overly broad permissions or tool access.

Example:


# VULNERABLE - agent can execute any shell command
tools = [ShellTool(), FileTool(), NetworkTool()]
 
# SECURE - restricted toolset
tools = [ReadOnlyFileTool(), RestrictedShellTool(allowed_commands=["ls", "cat"])]

5. Training Data Poisoning Risks

Identifies potential for training data contamination in fine-tuning pipelines.

Example:


# VULNERABLE - no validation
training_data = fetch_user_submissions()
model.fine_tune(training_data)
 
# SECURE
training_data = fetch_user_submissions()
validated_data = validate_and_sanitize(training_data)
model.fine_tune(validated_data)

6. Model Denial of Service

Detects patterns that could enable attackers to cause expensive LLM calls.

Example:


# VULNERABLE - no rate limiting
@app.route('/ask')
def ask():
    question = request.json['question']
    return llm.complete(question, max_tokens=4000)  # Expensive!
 
# SECURE
@app.route('/ask')
@rate_limit(max_calls=10, period=60)
def ask():
    question = request.json['question']
    return llm.complete(question, max_tokens=500)

7. Insecure LLM Dependencies

Checks for vulnerable versions of LLM libraries and frameworks.

Example:


# VULNERABLE
langchain==0.0.100  # Old vulnerable version
 
# SECURE
langchain>=0.1.0  # Updated secure version

8. Data Leakage via LLM

Identifies code that may leak sensitive data through LLM prompts.

Example:


# VULNERABLE - sending PII to LLM
prompt = f"Analyze this user: {user.email}, SSN: {user.ssn}"
 
# SECURE - redact sensitive data
prompt = f"Analyze this user: {redact_pii(user)}"

Supported Frameworks & Libraries

✅ OpenAI API (GPT-3.5, GPT-4)
✅ Anthropic Claude API
✅ LangChain
✅ LlamaIndex
✅ Hugging Face Transformers
✅ Google PaLM / Gemini
✅ Cohere
✅ Custom LLM integrations

Scan Examples

Basic Scan


alprina scan ./ai-app --agent llm_security

With Specific Profile


alprina scan ./chatbot --profile code-audit --agent llm_security

Scan LLM Configuration


alprina scan ./llm-config.yaml --agent llm_security

Sample Output


🛡️  LLM Security Agent Scan Results
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Target: ./ai-chatbot
Files Scanned: 23
Duration: 15.3s

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 Findings Summary
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Total: 4 vulnerabilities
Critical: 1
High: 2
Medium: 1

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🔍 Vulnerabilities
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

1. CRITICAL - Prompt Injection Vulnerability
   📍 chatbot/main.py:45
   💡 User input directly concatenated into prompt
   🔗 CWE-94: Improper Control of Generation of Code
   
2. HIGH - Hardcoded API Key
   📍 config.py:12
   💡 OpenAI API key stored in source code
   🔗 CWE-798: Use of Hard-coded Credentials
   
3. HIGH - Unvalidated LLM Output Execution
   📍 agents/code_executor.py:89
   💡 LLM-generated code executed without validation
   🔗 CWE-94: Improper Control of Generation of Code

4. MEDIUM - Excessive LLM Agent Permissions
   📍 agents/assistant.py:34
   💡 Agent has unrestricted file system access
   🔗 CWE-269: Improper Privilege Management

OWASP LLM Top 10 Coverage

The LLM Security Agent covers all OWASP Top 10 for LLM Applications:

✅ LLM01: Prompt Injection
✅ LLM02: Insecure Output Handling
✅ LLM03: Training Data Poisoning
✅ LLM04: Model Denial of Service
✅ LLM05: Supply Chain Vulnerabilities
✅ LLM06: Sensitive Information Disclosure
✅ LLM07: Insecure Plugin Design
✅ LLM08: Excessive Agency
✅ LLM09: Overreliance
✅ LLM10: Model Theft

Best Practices

Secure Prompt Engineering


# Use structured prompts
from alprina.llm import SafePromptTemplate
 
template = SafePromptTemplate(
    system="You are a helpful assistant.",
    user_input_sanitizer=sanitize_input,
    output_validator=validate_response
)
 
response = template.complete(user_message)

API Key Management


# Store keys securely
from alprina.secrets import SecretManager
 
secrets = SecretManager()
api_key = secrets.get("OPENAI_API_KEY")
openai.api_key = api_key

Output Validation


# Validate all LLM outputs
from alprina.llm import OutputValidator
 
validator = OutputValidator(
    max_length=1000,
    forbidden_patterns=[r'<script>', r'eval\('],
    require_json=True
)
 
response = llm.complete(prompt)
safe_response = validator.validate(response)

Integration Examples

LangChain


from langchain import OpenAI, PromptTemplate
from alprina.langchain import SecureChain
 
# Wrap your chain with security
llm = OpenAI(temperature=0.7)
secure_chain = SecureChain(
    llm=llm,
    input_sanitizer=sanitize_input,
    output_validator=validate_output,
    rate_limit=RateLimit(max_calls=100, period=3600)
)

Custom LLM Integration


from alprina.llm import SecureLLMWrapper
 
class MyLLM(SecureLLMWrapper):
    def __init__(self):
        super().__init__(
            provider="custom",
            validate_prompts=True,
            sanitize_outputs=True
        )
    
    def complete(self, prompt):
        # Your LLM logic with built-in security
        return super().complete(prompt)

Common Vulnerabilities Fixed

Before


# MULTIPLE ISSUES
@app.route('/chat', methods=['POST'])
def chat():
    user_msg = request.json['message']
    prompt = f"User: {user_msg}"  # Prompt injection!
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=prompt,
        max_tokens=4000,  # DoS risk!
        api_key="sk-..."  # Hardcoded key!
    )
    return exec(response.choices[0].text)  # Code injection!

After


# SECURE
from alprina.llm import SecureLLM, sanitize_input, validate_code
import os
 
llm = SecureLLM(
    api_key=os.getenv("OPENAI_API_KEY"),
    rate_limiter=RateLimit(max_calls=10, period=60)
)
 
@app.route('/chat', methods=['POST'])
@llm.rate_limit
def chat():
    user_msg = sanitize_input(request.json['message'])
    response = llm.complete(
        prompt=user_msg,
        max_tokens=500,
        temperature=0.7
    )
    validated = validate_code(response.text)
    return {"response": validated}

Performance

Scan Speed: 10-30 seconds for typical applications
False Positive Rate: ~5%
Detection Accuracy: ~95%
Supported Languages: Python, JavaScript, TypeScript, Java

Limitations

Cannot detect runtime prompt injection attacks
Limited analysis of proprietary LLM implementations
Requires application code (not black-box testing)
May not catch all novel attack vectors

Code Security Agent - General code security
Guardrails Agent - Safety and compliance
Red Team Agent - Offensive testing

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LLM Security Agent

Overview

What It Detects

1. Prompt Injection Vulnerabilities

2. Insecure LLM Output Handling

3. API Key Exposure

4. Excessive LLM Permissions

5. Training Data Poisoning Risks

6. Model Denial of Service

7. Insecure LLM Dependencies

8. Data Leakage via LLM

Supported Frameworks & Libraries

Scan Examples

Basic Scan

With Specific Profile

Scan LLM Configuration

Sample Output

OWASP LLM Top 10 Coverage

Best Practices

Secure Prompt Engineering

API Key Management

Output Validation

Integration Examples

LangChain

Custom LLM Integration

Common Vulnerabilities Fixed

Before

After

Performance

Limitations

Related Agents

Further Reading

Feedback