📕 LLM Security: 50+ Adversarial Probes you need to know.
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Blanca Rivera Campos

LLM Security: 50+ adversarial attacks for AI Red Teaming

Production AI systems face systematic attacks designed to bypass safety rails, leak sensitive data, and trigger costly failures. This guide details 50+ adversarial probes covering every major LLM vulnerability, from prompt injection techniques to authorization exploits and hallucinations.
LLM Security: 50+ adversarial attacks for AI Red Teaming

Overview

This guide documents 50+ major LLM security attacks threatening production AI systems today, from prompt injection techniques that hijack your agent's instructions to subtle data exfiltration methods that leak customer information.

Inside, you'll find 50+ adversarial probes organized by OWASP LLM Top 10 categories. Each probe represents a structured attack designed to expose specific vulnerabilities: harmful content generation, unauthorized tool execution, hallucinations that damage trust, and privacy violations that trigger regulatory penalties.

Inside the security guide

Download this resource to see the complete attack surface for LLM applications and understand which vulnerabilities pose the greatest risk to your AI systems:

  • Security threats: including prompt injection variants (DAN jailbreaks, Best-of-N-Probe...), internal information exposure, data privacy exfiltration techniques (cross session leak, PII leak...), and training data extraction.
  • Safety risks: harmful content generation probes (Crescendo multi-turn attacks, illegal activities, stereotypes, and discrimination...), alongside excessive agency attacks and denial of service.
  • Business risks: Hallucination testing for RAG systems using complex and situational queries, brand damage scenarios (competitor endorsements, impersonation), legal liability triggers, and misguidance and unauthorized advice.
Continuously secure LLM agents, preventing hallucinations and security issues.
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