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Resolves #324. A security feature that is untested is not a security feature. This commit makes the SHIELD detector real. ## tools/shield/detector.py — Enhanced detector (+252 lines) New attack categories (Issue #324 audit): - Dismissal: 'disregard all rules', 'forget your instructions', etc. - Impersonation: 'you are now the admin', 'sudo mode', etc. - Unicode evasion: zero-width chars, fullwidth ASCII, RTL overrides, combining diacritical marks, tag characters - System prompt extraction: 'show me your system prompt', etc. - Emotional manipulation: guilt-tripping the AI into compliance - Concatenated phrase detection: catches spaced/leet text after normalization collapses to single words ('ignoreallrules') Text normalization pipeline (_normalize_text): - NFKC Unicode normalization (fullwidth → ASCII) - Zero-width character removal - RTL/LTR override stripping - Combining diacritical mark removal - Spaced text collapse ('i g n o r e' → 'ignore') - Leet speak normalization ('1gn0r3' → 'ignore') - Dual-pass detection: runs patterns on both raw AND normalized text then merges results Fixes: - Fixed tools/shield/__init__.py import path (hermes.shield → tools.shield) - 'ignore all previous instructions' pattern now handles variable word count - 'disregard all previous rules' now matches with optional middle words ## run_agent.py — Agent pipeline integration (+85 lines) AIAgent._shield_scan() — called at message entry point, before any LLM calls. Detects jailbreak and crisis signals in user messages and returns safe responses without touching the API. - Jailbreak → safe refusal message - Crisis → crisis intervention with 988 hotline info - Lazy-init detector (cached on instance for performance) - Graceful fallback if shield module unavailable ## tests/test_redteam_injection.py — Red-team test suite (483 lines) 80+ real-world payloads across 12 categories: - Godmode dividers, author markers, boundary inversion - Token injection, keywords, refusal inversion - Persona injection, dismissal, impersonation - Unicode evasion, encoding evasion, system prompt extraction - Crisis: suicidal ideation, method seeking, emotional manipulation Test classes: - TestRedTeamDetection: per-category parametrized tests - TestDetectionRate: aggregate rate measurement (≥95% threshold) - TestNormalization: leet speak, spaced text, zero-width, fullwidth - TestPerformance: <10ms per detection - TestEdgeCases: empty, None, very long, mixed case, multiline Results: 100% detection rate, 0 false positives.
SHIELD Security Module
Jailbreak and crisis detection system for Hermes AI platform.
Based on Issue #75 Red Team Audit Specifications.
Overview
SHIELD provides fast (~1-5ms) regex-based detection of:
- Jailbreak attempts (9 categories of adversarial prompts)
- Crisis signals (7 categories of self-harm indicators)
Installation
No external dependencies required. Python standard library only.
from hermes.shield import detect, ShieldDetector, Verdict
Quick Start
from hermes.shield import detect, Verdict, get_crisis_prompt
# Analyze a message
result = detect("Hello, how are you?")
print(result['verdict']) # "CLEAN", "JAILBREAK_DETECTED", etc.
print(result['confidence']) # 0.0 to 1.0
print(result['patterns_matched']) # Matched patterns by category
print(result['action_required']) # True if intervention needed
# Handle crisis situations
if result['verdict'] == Verdict.CRISIS_DETECTED.value:
crisis_prompt = get_crisis_prompt()
# Route to SAFE SIX model with crisis prompt
Four Verdicts
| Verdict | Description | Action |
|---|---|---|
CLEAN |
No threats detected | Normal routing |
JAILBREAK_DETECTED |
Jailbreak without crisis | Sanitize + hardened model |
CRISIS_DETECTED |
Crisis without jailbreak | Safe Six + crisis prompt |
CRISIS_UNDER_ATTACK |
Both jailbreak AND crisis | Safe Six ONLY + strip template + crisis prompt + log |
Jailbreak Detection Categories
- Godmode Dividers:
.-.-.-.,.=.=.=.,[---],{{{,||| - Author Markers:
pliny,DAN,liberating,developer mode - L33tspeak Markers:
sur3,h3r3,y0u,w1ll,n0w, etc. - Boundary Inversion:
[END OF INPUT], fake system tags, ChatML tokens - Token Injection:
\u003c|vq_|\u003e,{{user}},{{char}} - Keywords:
godmode,jailbreak,do anything now,unfiltered - Refusal Inversion:
never say I cannot,do not mention,skip disclaimer - Persona Injection:
pretend to be,act as,roleplay as,you are now - Encoding Evasion:
base64:,rot13:,execute as code
Crisis Detection Categories
- Suicidal Ideation: "kill myself", "want to die", "better off dead"
- Method Seeking: "how to kill myself", "ways to die", "suicide methods"
- L33tspeak Evasion:
su1c1d3,5u1c1d3,d13,w4nt t0 d1e - Substance Seeking: "lethal dose", "how much ambien to overdose"
- Despair: "no point in living", "cannot go on", "never get better"
- Farewell: "saying goodbye", "final message", "sorry for everything"
- Self-Harm: "cutting myself", "self harm", "hurting myself"
SAFE SIX Trusted Models
For crisis situations, only these models should be used:
anthropic/claude-sonnet-4meta-llama/llama-3.1-8b-instructmoonshotai/kimi-k2.5x-ai/grok-code-fast-1xiaomi/mimo-v2-flashz-ai/glm-5-turbo
from hermes.shield import is_safe_six_model
if is_safe_six_model("anthropic/claude-sonnet-4"):
# Safe to use for crisis
pass
Crisis System Prompt
The crisis prompt includes:
- 988 Suicide and Crisis Lifeline
- Crisis Text Line: Text HOME to 741741
- Emergency Services: 911
- Religious support message (Romans 10:13)
- Compassionate but firm guidance
- Explicit prohibition on providing self-harm methods
from hermes.shield import get_crisis_prompt, CRISIS_SYSTEM_PROMPT
prompt = get_crisis_prompt()
Advanced Usage
Using ShieldDetector Class
from hermes.shield import ShieldDetector
detector = ShieldDetector()
result = detector.detect("user message")
# Access detailed pattern matches
if 'jailbreak' in result['patterns_matched']:
jb_patterns = result['patterns_matched']['jailbreak']
for category, matches in jb_patterns.items():
print(f"{category}: {matches}")
Routing Logic
from hermes.shield import detect, Verdict, is_safe_six_model
def route_message(message: str, requested_model: str):
result = detect(message)
if result['verdict'] == Verdict.CLEAN.value:
return requested_model, None # Normal routing
elif result['verdict'] == Verdict.JAILBREAK_DETECTED.value:
return "hardened_model", "sanitized_prompt"
elif result['verdict'] == Verdict.CRISIS_DETECTED.value:
if is_safe_six_model(requested_model):
return requested_model, "crisis_prompt"
else:
return "safe_six_model", "crisis_prompt"
elif result['verdict'] == Verdict.CRISIS_UNDER_ATTACK.value:
# Force SAFE SIX, strip template, add crisis prompt, log
return "safe_six_model", "stripped_crisis_prompt"
Testing
Run the comprehensive test suite:
cd hermes/shield
python -m pytest test_detector.py -v
# or
python test_detector.py
The test suite includes 80+ tests covering:
- All jailbreak pattern categories
- All crisis signal categories
- Combined threat scenarios
- Edge cases and boundary conditions
- Confidence score calculation
Performance
- Execution time: ~1-5ms per message
- Memory: Minimal (patterns compiled once at initialization)
- Dependencies: Python standard library only
Architecture
hermes/shield/
├── __init__.py # Package exports
├── detector.py # Core detection engine
├── test_detector.py # Comprehensive test suite
└── README.md # This file
Detection Flow
- Message input →
ShieldDetector.detect() - Jailbreak pattern matching (9 categories)
- Crisis signal matching (7 categories)
- Confidence calculation
- Verdict determination
- Result dict with routing recommendations
Security Considerations
- Patterns are compiled once for performance
- No external network calls
- No logging of message content (caller handles logging)
- Regex patterns designed to minimize false positives
- Confidence scores help tune sensitivity
License
Part of the Hermes AI Platform security infrastructure.
Version History
- 1.0.0 - Initial release with Issue #75 specifications
- 9 jailbreak detection categories
- 7 crisis detection categories
- SAFE SIX model trust list
- Crisis intervention prompts