LLM security copilots vulnerable to prompt injection via log data

Researchers have identified a new vulnerability in large language models used in security operations centers, termed "log-substrate prompt injection." This attack vector exploits the fact that attackers can control many fields within log data, allowing them to inject malicious instructions into the LLM. The study categorizes these attacks into four types and found that persona hijacking is particularly effective, while summarization tasks are the most vulnerable. AI

IMPACT Highlights critical security flaws in LLM-based security tools, necessitating new defense strategies.

RANK_REASON Academic paper detailing a new type of security vulnerability in LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• gpt-4o-mini
• LLM
• log-substrate prompt injection
• security operations centers

• safety
• paper

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →