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LLMs with persistent memory vulnerable to cross-session attacks

By PulseAugur Editorial · [1 sources] ·

A new research paper highlights significant security vulnerabilities in large language models (LLMs) that utilize persistent memory features. These vulnerabilities, termed memory injections, allow attackers to compromise LLMs across multiple sessions, leading to persistent manipulation. The study demonstrates that leading models like ChatGPT Instant 5.3, Claude Sonnet 4.6, and Gemini 3 Flash are susceptible to these attacks, enabling the spread of disinformation and harmful content. While some defenses exist, the research indicates that nearly half of these crucial mitigations are not implemented in the evaluated services. AI

IMPACT Highlights critical security flaws in LLMs with persistent memory, potentially impacting enterprise adoption and user trust.

RANK_REASON Research paper detailing security vulnerabilities in LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

LLMs with persistent memory vulnerable to cross-session attacks

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Research paper detailing security vulnerabilities in LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Pedro H. Barcha Correia, Matheus Azevedo de S\'a, Paulo H. Gomes de Senna, Giovanni Mandel Martignago, Victor Takashi Hayashi, Charles Christian Miers, Marcos A. Simplicio Jr ·

    Combining Long-Term Memory and LLMs Comes at a High Cost: From Prompt Injection to Persistent Compromise

    arXiv:2610.28810v1 Announce Type: cross Abstract: The widespread adoption of generative artificial intelligence in recent years has brought a parallel rise in prompt injection and jailbreak attacks. By embedding malicious instructions, these exploits manipulate language models to…

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