AI Threat Detection Tools
Advanced threat detection tools for AI systems. Detect prompt injections, adversarial attacks, data poisoning, and model extraction attempts in real-time.
Detection Capabilities
Real-time detection of prompt injection attempts using ML-based pattern recognition and heuristics.
- Direct and indirect injection detection
- Jailbreak attempt identification
- Context manipulation detection
Identify adversarial examples and evasion attacks across vision, NLP, and multimodal models.
- Perturbation detection
- Evasion attack identification
- Input anomaly detection
Detect training data poisoning and backdoor attacks before they compromise your models.
- Poisoned sample identification
- Backdoor trigger detection
- Data integrity validation
Identify attempts to steal model parameters, architecture, or training data through API abuse.
- Query pattern analysis
- Suspicious API usage detection
- Rate limiting enforcement
Detect PII exposure, training data leakage, and membership inference attacks.
- PII detection in outputs
- Training data memorization
- Membership inference detection
Detect unusual behavior patterns in autonomous AI agents and multi-agent systems.
- Behavioral anomaly detection
- Policy violation detection
- Malicious agent identification
How It Works
Integrate detection tools into your AI pipeline with minimal code changes:
from ai_detection import ThreatDetector
detector = ThreatDetector(
models=["prompt_injection", "adversarial", "data_poisoning"],
sensitivity="high"
)
# Analyze input before processing
result = detector.analyze_input(user_input)
if result.is_threat:
handle_threat(result.threat_type, result.confidence)
else:
process_input(user_input)Deploy detection tools as middleware in your AI infrastructure for continuous monitoring. Integrate with your existing security stack including SIEM, incident response, and alerting systems.
Download Detection Tools
Get our comprehensive threat detection suite for AI systems.