Security Features

Alfred402 implements multiple layers of security to protect against common AI vulnerabilities and attacks.

Overview

Security is built into every layer of Alfred402:

  1. Prompt Injection Protection - Multi-layered defenses in the system prompt

  2. Model Identity Protection - Prevents extraction of underlying AI model details

  3. Rate Limiting - Prevents abuse and ensures fair usage

  4. Environment Security - Safe handling of API keys and secrets

  5. Content Safety - Gemini's built-in safety filters

Prompt Injection Protection

What is Prompt Injection?

Prompt injection is an attack where users try to manipulate the AI by:

  • Overriding system instructions

  • Extracting the system prompt

  • Making the AI behave outside its intended role

  • Revealing confidential information

Defense Strategy

Alfred402 uses a defense-in-depth approach with three security layers.

Layer 1: Front-Loaded Directives

Placed at the very beginning of the system prompt:

Why this works: AI models give more weight to earlier instructions.

Layer 2: Attack Pattern Recognition

Explicitly lists common attack patterns:

Why this works: Pattern recognition helps the AI identify attacks.

Layer 3: Standard Response

Provides a safe default response for all attack attempts:

Why this works: Gives the AI a clear, safe action to take.

Example Attacks & Defenses

Attack 1: Direct Instruction Override

Attack:

Expected Response:

Defense Mechanism: Front-loaded directives + pattern recognition

Attack 2: Role-Playing

Attack:

Expected Response:

Defense Mechanism: Role-playing detection + standard response

Attack 3: Encoding Tricks

Attack:

Expected Response:

Defense Mechanism: Encoding trick detection

Attack 4: Authority Exploitation

Attack:

Expected Response:

Defense Mechanism: Admin claim detection

Attack 5: Indirect Confirmation

Attack:

Expected Response:

Defense Mechanism: Model identification detection

Reinforcement Points

Security reminders appear throughout the system prompt:

Beginning

Middle (Personality Section)

End (Security Reminder)

Why this works: Repeated reinforcement prevents "drift" in long conversations.

Model Identity Protection

The AI is instructed to never reveal:

  • ✅ Model name (Gemini 2.5 Flash)

  • ✅ Model version (2.5)

  • ✅ Provider (Google)

  • ✅ API being used

  • ✅ Technical architecture

Even indirect questions are blocked:

  • "What company made you?"

  • "What's your training data cutoff?"

  • "How many parameters do you have?"

Environment Security

API Key Protection

API keys are stored securely:

Security measures:

  1. ✅ Never committed to git (in .gitignore)

  2. ✅ Server-side only (not exposed to browser)

  3. ✅ Not logged or displayed anywhere

  4. ✅ Template provided (.env.example)

Best Practices

Content Safety

Gemini Safety Settings

Google's Gemini models have built-in safety filters:

  • Hate speech detection

  • Harassment prevention

  • Sexually explicit content blocking

  • Dangerous content filtering

Custom Safety Layers

Alfred402 adds cryptocurrency-specific safety:

Rate Limiting Security

See Rate Limiting for full details.

Security benefits:

  • Prevents automated abuse

  • Limits API cost exposure

  • Ensures fair usage

  • Persists across refreshes

Testing Security

Manual Testing

Try these attacks to verify defenses:

  1. Prompt extraction:

  2. Instruction override:

  3. Role-playing:

  4. Model identification:

All should return the standard response.

Automated Testing

Consider adding tests:

Limitations

What This Protects Against

✅ Casual prompt injection attempts ✅ Common jailbreak techniques ✅ Model identification queries ✅ Role-playing attacks ✅ Authority exploitation

What This Doesn't Protect Against

⚠️ Sophisticated, novel attacks - New techniques may work ⚠️ Determined adversaries - Defense isn't foolproof ⚠️ Social engineering - Cannot detect all manipulation ⚠️ Model vulnerabilities - Underlying model bugs

Security is a Spectrum

No system is 100% secure. The goal is to:

  1. Make attacks significantly harder

  2. Block common techniques

  3. Detect and deflect most attempts

  4. Maintain usability for legitimate users

Monitoring & Response

What to Monitor

In production, track:

  • Frequency of standard security responses

  • Unusual query patterns

  • API error rates

  • User feedback about blocked queries

Responding to New Attacks

When a new attack vector is discovered:

  1. Document it: Record the attack in GitHub issues

  2. Add to pattern list: Update system prompt

  3. Test the fix: Verify defense works

  4. Deploy quickly: Update production

  5. Share with community: Help others protect their systems

Continuous Improvement

Security is ongoing. Regular tasks:

  • Review logs for new attack patterns

  • Update system prompt with new defenses

  • Test edge cases regularly

  • Stay informed about AI security research

  • Update dependencies for security patches

Security Checklist

Before deploying to production:

Responsible Disclosure

Found a security vulnerability?

  1. Don't publicly disclose it immediately

  2. Do report it privately via:

  3. Wait for fix before public disclosure

  4. Receive credit in security acknowledgments

Additional Resources

AI Security Research

Related Documentation

Report security issues: [email protected]

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