[2602.15654] Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections

Skip to main content

arXiv is now an independent nonprofit!<br>Learn more<br>&times;

Search arXiv

Press Enter to search &middot; Advanced search

-->

Computer Science > Cryptography and Security

arXiv:2602.15654 (cs)

[Submitted on 17 Feb 2026 (v1), last revised 5 Mar 2026 (this version, v2)]

Title:Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections

Authors:Xianglin Yang, Yufei He, Shuo Ji, Bryan Hooi, Jin Song Dong<br>View a PDF of the paper titled Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections, by Xianglin Yang and 4 other authors

View PDF<br>HTML (experimental)

Abstract:Self-evolving LLM agents update their internal state across sessions, often by writing and reusing long-term memory. This design improves performance on long-horizon tasks but creates a security risk: untrusted external content observed during a benign session can be stored as memory and later treated as instruction. We study this risk and formalize a persistent attack we call a Zombie Agent, where an attacker covertly implants a payload that survives across sessions, effectively turning the agent into a puppet of the attacker.

We present a black-box attack framework that uses only indirect exposure through attacker-controlled web content. The attack has two phases. During infection, the agent reads a poisoned source while completing a benign task and writes the payload into long-term memory through its normal update process. During trigger, the payload is retrieved or carried forward and causes unauthorized tool behavior. We design mechanism-specific persistence strategies for common memory implementations, including sliding-window and retrieval-augmented memory, to resist truncation and relevance filtering. We evaluate the attack on representative agent setups and tasks, measuring both persistence over time and the ability to induce unauthorized actions while preserving benign task quality. Our results show that memory evolution can convert one-time indirect injection into persistent compromise, which suggests that defenses focused only on per-session prompt filtering are not sufficient for self-evolving agents.

Comments:<br>Published as a workshop paper in Lifelong Agent @ ICLR 2026

Subjects:

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2602.15654 [cs.CR]

(or<br>arXiv:2602.15654v2 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2602.15654

Focus to learn more

arXiv-issued DOI via DataCite

Submission history<br>From: Xianglin Yang [view email]<br>[v1]<br>Tue, 17 Feb 2026 15:28:24 UTC (459 KB)

[v2]<br>Thu, 5 Mar 2026 11:36:38 UTC (460 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections, by Xianglin Yang and 4 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.CR

next >

new<br>recent<br>| 2026-02

Change to browse by:

cs<br>cs.AI

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Which authors of this paper are endorsers? |<br>Disable...