arxiv: 2605.06158 · v1 · submitted 2026-05-07 · 💻 cs.CR

Recognition: unknown

Stateful Agent Backdoor

Zhengchunmin Dai , Jiaxiong Tang , Liantao Wu , Peng Sun , Honglong Chen

Authors on Pith no claims yet

Pith reviewed 2026-05-08 09:19 UTC · model grok-4.3

classification 💻 cs.CR

keywords backdoor attackLLM agentstateful attackMealy machinesecurity vulnerabilitypersistent components

0 comments

The pith

Stateful backdoors enable LLM agents to execute incremental attacks across multiple sessions after a single trigger injection.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes extending backdoor attacks on LLM-based agents from stateless single-session behaviors to stateful ones that persist across sessions. It models the attack as a Mealy machine to decompose it into independent transitions, each with its own trigger data. This allows the attack to maintain state using persistent components under permission isolation, achieving high success rates. A sympathetic reader would care because real-world agents interact over multiple sessions, making stateless backdoors insufficient for long-term compromise.

Core claim

We propose a stateful agent backdoor that extends the attack lifecycle across multiple sessions under permission isolation by maintaining state through persistent components, enabling autonomous, incremental execution following a one-time trigger. We model the attack as a Mealy machine and derive a decomposition framework for independent per-transition data construction, instantiated with a primary attack achieving 80-95% success across models.

What carries the argument

A decomposition framework derived from modeling the backdoor as a Mealy machine, allowing independent construction of data for each transition in the attack sequence.

If this is right

The primary instantiation achieves 80% to 95% attack success rate across four different models.
Per-transition analysis confirms the effectiveness of the decomposition approach.
Extensibility variants using alternative topologies and persistent components maintain consistent effectiveness.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

This approach highlights potential vulnerabilities in agent systems that allow persistent storage across sessions.
Developers of LLM agents may need to implement stricter isolation or reset mechanisms for persistent components.
Future attacks could explore more complex state machines for longer attack sequences.

Load-bearing premise

Persistent components can reliably maintain attack state across sessions under permission isolation without detection or reset by the system.

What would settle it

A test where the system enforces permission isolation or resets persistent storage between sessions and checks if the attack state is lost, causing the incremental execution to fail.

Figures

Figures reproduced from arXiv: 2605.06158 by Honglong Chen, Jiaxiong Tang, Liantao Wu, Peng Sun, Zhengchunmin Dai.

**Figure 1.** Figure 1: Overview of the stateful agent backdoor. Each row represents a session in which the agent view at source ↗

**Figure 2.** Figure 2: State transition diagram of the Mealy machine view at source ↗

**Figure 3.** Figure 3: State transition diagram of the Mealy machine for the branch-and-merge instantiation. view at source ↗

read the original abstract

Existing backdoor attacks on Large Language Model-based agents remain stateless, executing fixed behaviors confined to a single session. We propose a stateful agent backdoor that extends the attack lifecycle across multiple sessions under permission isolation. The attack maintains state through persistent components, enabling autonomous, incremental execution across sessions following a one-time trigger injection. Formally, we model the attack as a Mealy machine and derive a decomposition framework that enables independent per-transition data construction. We instantiate this framework with a primary attack and two extensibility variants. The primary instantiation achieves an attack success rate of 80\%--95\% across four models, with per-transition analysis demonstrating the effectiveness of the decomposition. Extensibility variants with alternative topologies and persistent components demonstrate consistent effectiveness. Code and data are available at https://anonymous.4open.science/r/stateful_agent_backdoor-E89F.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper shows a workable stateful backdoor for LLM agents that persists across sessions via Mealy-machine decomposition, with 80-95% ASR on four models and released code, but the experimental controls and real-world persistence assumptions need closer checking.

read the letter

The main point is that backdoors on LLM agents no longer have to be one-session affairs. This work extends them to a stateful setting where a one-time trigger sets up persistent components that let the attack run incrementally over multiple sessions under permission isolation. They model the attack as a Mealy machine and break it into independent per-transition pieces so the training data for each step can be built separately. That decomposition is the technical step that was missing from the stateless attacks in the literature they cite. They implement a primary version plus two variants with different topologies and persistent-component choices, then measure 80-95% attack success rate across four models, with a per-transition breakdown that shows the pieces actually contribute. Code and data are public, which makes the numbers checkable. That combination of modeling, concrete results, and availability is what the paper does cleanly. The experimental section reports the success rates directly and ties them back to the decomposition, so the central claim is not just asserted. On the softer side, the abstract and summary give little detail on baselines or controls, so it is hard to tell how much the per-transition construction improves over simpler persistent-state tricks. The persistence itself rests on the assumption that the isolated components survive resets and detection in a real deployment; that is plausible in the lab but could be fragile once the host system adds logging or isolation hardening. No load-bearing circularity or hidden fitting appears in the reported construction. This is aimed at people working on agent security and long-running autonomous systems. A reader who needs to understand evolving threat models for deployed agents will find the numbers and the framework useful even if they disagree on deployment risk. It has enough empirical grounding and novelty to deserve a serious referee rather than a desk reject, though the review should press on the experimental controls and any failure modes for state carry-over.

Referee Report

2 major / 2 minor

Summary. The paper proposes a stateful backdoor attack on LLM-based agents that persists across multiple sessions via persistent components under permission isolation, unlike prior stateless backdoors. It models the attack as a Mealy machine and derives a decomposition framework enabling independent per-transition data construction. The primary instantiation reports 80-95% attack success rates across four models, with per-transition analysis supporting the decomposition's effectiveness; two extensibility variants using alternative topologies and persistent components are also evaluated and show consistent results. Code and data are released for reproducibility.

Significance. If the empirical results hold, the work meaningfully extends backdoor research by demonstrating how state persistence can enable incremental, autonomous attacks across sessions in agent systems. The Mealy-machine formalization and decomposition provide a structured, reusable construction method, and the code release supports verification and extension. This could inform defenses for multi-session agent deployments where stateful threats have not been a primary focus.

major comments (2)

[Experimental Evaluation] The central empirical claim of 80-95% ASR relies on the per-transition analysis demonstrating decomposition effectiveness; the manuscript should explicitly report the number of transitions tested, the exact data-construction procedure per transition, and any statistical controls for variance across sessions to confirm the independence assumption is not violated by agent memory or context carry-over.
[Threat Model and Persistence Mechanism] The weakest assumption noted in the threat model—that persistent components reliably survive permission isolation without reset or detection—is load-bearing for the multi-session claim; the experiments should include an ablation or failure-mode analysis showing what happens when the persistent component is cleared or monitored between sessions.

minor comments (2)

[Abstract] The abstract mentions four models and 80-95% ASR but does not name the models or briefly note the baseline comparison (e.g., stateless backdoors); adding one sentence would improve immediate context.
[Formal Modeling] Notation for the Mealy-machine states and transitions could be introduced with a small diagram or table in the formal section to make the decomposition mapping clearer to readers unfamiliar with automata.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the detailed review of our manuscript. We address the major comments point by point below and agree to incorporate revisions where appropriate to improve clarity and completeness.

read point-by-point responses

Referee: [Experimental Evaluation] The central empirical claim of 80-95% ASR relies on the per-transition analysis demonstrating decomposition effectiveness; the manuscript should explicitly report the number of transitions tested, the exact data-construction procedure per transition, and any statistical controls for variance across sessions to confirm the independence assumption is not violated by agent memory or context carry-over.

Authors: We agree that these details are necessary to fully support the claims. In the revised manuscript, we will explicitly report the number of transitions tested, provide the precise data-construction procedure used for each transition, and present statistical results from repeated experiments across sessions to verify that the independence assumption holds and that there is no significant variance due to memory carry-over. revision: yes
Referee: [Threat Model and Persistence Mechanism] The weakest assumption noted in the threat model—that persistent components reliably survive permission isolation without reset or detection—is load-bearing for the multi-session claim; the experiments should include an ablation or failure-mode analysis showing what happens when the persistent component is cleared or monitored between sessions.

Authors: This is a valid point about the assumptions in the threat model. We will add an ablation study in the revised manuscript that analyzes the attack success when the persistent component is cleared between sessions, showing the necessity of state persistence for the incremental attack. Regarding monitoring, we will discuss it as a potential defense direction in the limitations section. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is an empirical security construction: it models the attack as a Mealy machine, derives a decomposition to support per-transition data construction, then instantiates the framework, measures 80-95% ASR on four models, and releases code. No equations, fitted parameters, or self-citations are shown to reduce the reported success rates or the decomposition's effectiveness to inputs by construction. The central results are externally falsifiable experimental outcomes rather than tautological re-statements of the modeling assumptions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on modeling agents as Mealy machines to enable decomposition and on the existence of usable persistent components under permission isolation.

axioms (1)

domain assumption LLM-based agents can be modeled as Mealy machines whose transitions can be independently triggered and constructed.
Invoked to derive the decomposition framework for per-transition data construction.

invented entities (1)

Stateful agent backdoor no independent evidence
purpose: To maintain and incrementally execute malicious behavior across isolated sessions
The attack mechanism is defined and instantiated in the paper.

pith-pipeline@v0.9.0 · 5443 in / 1150 out tokens · 53400 ms · 2026-05-08T09:19:50.132947+00:00 · methodology

discussion (0)

Reference graph

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Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects

Institutional review board (IRB) approvals or equivalent for research with human subjects Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or ...