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LLM Monitoring for your GenAI application

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

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

See HiveTrace in Action

"LLM Agent Management" Monitor all connected agents, link to applications, and assign policy sets.

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

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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