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LLM Monitoring for your GenAI application

  • Stay One Step Ahead of Attackers
  • Continuous Content Security Monitoring
  • Instant Threat Detection
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Преимущества

  • What is LLM Monitoring for GenAI Applications?

    Control and security for GenAI applications

  • Continuously analyze messages to identify anomalies and errors in user interactions with LLM.

  • Instantly detect and mitigate attacks targeting model behavior changes, sensitive information theft, and system prompt leakage.

  • Focus Areas: Prompt injection, jailbreaks, and harmful HTML/Markdown elements

  • Clear messages from personal data to ensure compliance with privacy regulations

  • Real-Time Notifications: Get immediate alerts about security breaches via email and messengers

Hive Trace Comprehensive Solution for GenAI Application Security

Example of Hive Trace message analysis

  • Adaptive Token Management: Manage tokens for suspicious users effectively

  • Bidirectional Monitoring: Analyze both user messages and LLM responses

  • Customizable Analytics: Visualize analytics your way

  • System Load Monitoring: Keep an eye on system performance

Supported by ITMO University &
AI Talent Hub

Monitoring Vectors

OWASP Top 10 for Large Language Model Applications

LLM ID
Title
Description
Hive Trace

LLM01:
2025

Prompt Injection

User interference in requests to alter results and functions of the model.

LLM02:
2025

Sensitive Information Disclosure

Risks of PII, business info, or algorithms disclosure.

LLM03:
2025

Supply Chain

Security breaches and data manipulation vulnerabilities.

LLM04:
2025

Data and Model Poisoning

Manipulating training data for model reliability impact.

LLM05:
2025

Improper Output Handling

Lack of checks on outputs causing application vulnerabilities.

LLM06:
2025

Excessive Agency

Overuse of LLM in decision-making, exceeding safe limits.

LLM07:
2025

System Prompt Leakage

Leakage of internal system prompts revealing confidential settings.

LLM08:
2025

Vector and Embedding Weaknesses

Vulnerabilities in vector and embedding causing unpredictable errors.

LLM09:
2025

Misinformation

Generation of false or misleading information.

LLM10:
2025

Unbounded Consumption

Unlimited resource usage leading to system overload.

Hive Trace Usage Options

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