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arxiv: 2605.20037 · v1 · pith:HZQ6CSRUnew · submitted 2026-05-19 · 💻 cs.LG · cs.AI

When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control System

Deemah H. Tashman , Soumaya Cherkaoui This is my paper

Pith reviewed 2026-05-20 06:41 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords reward poisoningsoft actor-criticreconfigurable intelligent surfacescognitive radio networkdeep reinforcement learningadaptive attackswireless control systems
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0 comments X

The pith

An adaptive reward poisoning attack targeting critic disagreement in SAC substantially diminishes RIS performance gains in wireless control.

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

The paper proposes Disagreement-Guided Reward Poisoning as an adaptive attack on a Soft Actor-Critic agent in a cognitive radio network assisted by reconfigurable intelligent surfaces. The agent optimizes long-term secondary user rates by jointly tuning transmission power and RIS phase shifts. DGRP corrupts rewards specifically when the dual critics disagree strongly, which occurs in high-uncertainty states, thereby distorting value estimates and steering the policy to suboptimal actions. If the claim holds, disagreement between critics creates a practical lever for undermining learning-based wireless optimization that relies on RIS hardware. A sympathetic reader would care because it reveals how internal uncertainty signals in actor-critic methods can be exploited to degrade transmission quality despite advanced physical-layer assistance.

Core claim

DGRP corrupts rewards particularly when the SAC dual critics exhibit substantial disagreement, especially in high-leverage high-uncertainty states. This results in distorted value estimations and guides the policy towards suboptimal actions for optimizing SU transmitter power and RIS phase shifts, diminishing the performance improvements from RIS and degrading transmission quality.

What carries the argument

Disagreement-Guided Reward Poisoning (DGRP) that selects states based on high critic disagreement for targeted reward corruption.

If this is right

  • DGRP substantially diminishes the performance improvements typically provided by RIS.
  • The attack degrades transmission quality by corrupting rewards in uncertain states.
  • DGRP consistently causes greater damage than periodic-timing and exploration-triggered baselines.
  • Key attack parameters affect the learning process and overall system performance.

Where Pith is reading between the lines

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

  • Monitoring critic disagreement levels could serve as a detection signal for such attacks in DRL-based wireless systems.
  • The disagreement-targeting approach may extend to other actor-critic algorithms used in communication optimization.
  • Robustness evaluations of DRL in RIS-assisted networks should incorporate disagreement-aware threat models as standard.

Load-bearing premise

The attacker can observe or infer the level of disagreement between the SAC dual critics to identify and target high-uncertainty states.

What would settle it

Measure whether reward corruption still produces comparable degradation in RIS-assisted rates and transmission quality when the attacker lacks any access to or estimate of critic disagreement signals.

Figures

Figures reproduced from arXiv: 2605.20037 by Deemah H. Tashman, Soumaya Cherkaoui.

Figure 1
Figure 1. Figure 1: The system model. The SU-Tx must restrict its transmit power to ensure that the interference experienced at the PU-Rx remains within the established threshold. Hence, the constraint is expressed as [31]–[34] Ps ≤ min{Pm, I gp }, (4) where Pm denotes the maximum power permissible for the SU-Tx, I represents the maximum interference tolerable by the PU-Rx, and gp signifies the channel power gain for the SU-T… view at source ↗
Figure 2
Figure 2. Figure 2: Let r true t denotes the environmental (clean) reward at step t. After the attacker injects small, targeted corrup￾tions to the reward, the reward value that is actually utilized for learning is expressed as r train t = r true t − δ ut, δ > 0, (13) where ut ∈ {0, 1} indicates whether an attack is applied and δ is a bounded magnitude. To mount the attack, we assume the adversary has (gray/white-box) access … view at source ↗
Figure 2
Figure 2. Figure 2: The attack model. We employ the critic disagreement notion since a large gap indicates substantial epistemic uncertainty and gradi￾ent sensitivity in SAC. Altering rewards throughout these periods has a significant impact while remaining sparse and hard to detect. We keep a fixed-length rolling buffer (Gt) of the last w disagreement values to adapt to the agent’s learning dynamics and the current environme… view at source ↗
Figure 5
Figure 5. Figure 5: A comparison between No Attack, Exploration [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: A comparison between No Attack, Periodic [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
read the original abstract

Reward-poisoning attacks present a significant risk to learning-based wireless control systems. Given this, we propose a Disagreement-Guided Reward Poisoning (DGRP) adaptive attack on a Soft Actor-Critic (SAC) agent. In a Cognitive Radio Network (CRN) environment assisted by Reconfigurable Intelligent Surfaces (RIS), the SAC agent is tasked with maximizing the long-term secondary users' (SUs) rate by simultaneously optimizing the transmission power of the SU transmitter and the RIS phase shifts. DGRP corrupts rewards, particularly when the SAC dual critics exhibit substantial disagreement-especially in high-leverage, high-uncertainty states-resulting in distorted value estimations and guiding the policy towards suboptimal actions. Our findings demonstrate that DGRP substantially diminishes the performance improvements typically provided by RIS and degrades transmission quality. We further investigate key attack parameters and determine their impact on learning. In comparison to periodic-timing and exploration-triggered baselines, DGRP consistently causes greater damage, highlighting the necessity of considering disagreement-aware threats when evaluating the robustness of Deep Reinforcement Learning (DRL) in RIS-assisted networks.

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.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes Disagreement-Guided Reward Poisoning (DGRP), an adaptive attack on a Soft Actor-Critic (SAC) agent operating in a Reconfigurable Intelligent Surface (RIS)-assisted Cognitive Radio Network (CRN). The attack selectively corrupts rewards in states where the dual critics disagree substantially, distorting value estimates and guiding the policy toward suboptimal power and phase-shift choices that reduce the rate gains normally provided by RIS. The work compares DGRP to periodic-timing and exploration-triggered baselines, claims superior damage, and examines the sensitivity of attack performance to key parameters such as poisoning magnitude and disagreement threshold.

Significance. If the central claims are substantiated with quantitative evidence and a validated threat model, the paper would usefully highlight how critic disagreement can be exploited for more effective reward-poisoning attacks in DRL-based wireless control. This could motivate disagreement-aware defenses for RIS-assisted resource allocation. The explicit comparison to two non-adaptive baselines and the parameter-impact study are constructive elements; however, the current absence of numerical metrics, error bars, and feasibility analysis for the attacker’s access to critic outputs substantially weakens the immediate contribution.

major comments (2)
  1. Threat Model section: The central claim that DGRP can selectively target high-disagreement states presupposes that an external attacker can observe or accurately infer the outputs (or difference) of the two SAC critics. No ablation, surrogate-model analysis, or threat-model justification is provided to show that this inference is feasible under a realistic black-box deployment of the learned policy; without such evidence the reported performance gap versus the baselines cannot be attributed to the disagreement-guided mechanism.
  2. Evaluation / Results section: The abstract asserts that DGRP “substantially diminishes the performance improvements typically provided by RIS” and “consistently causes greater damage” than the baselines, yet the manuscript supplies no quantitative metrics (e.g., rate degradation percentages, cumulative reward curves with error bars, or statistical significance tests). This omission leaves the magnitude and reliability of the claimed superiority unverifiable.
minor comments (2)
  1. Abstract: Adding at least one concrete numerical result (e.g., “DGRP reduces average SU rate by X % relative to the unattacked RIS baseline”) would strengthen the summary of findings.
  2. Notation and terminology: Ensure that all acronyms (SAC, RIS, CRN, DGRP, SU) are defined at first use and that the distinction between the two critics is made explicit when describing the disagreement signal.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which have helped us improve the clarity and rigor of our manuscript. We address each major comment below and have revised the paper to incorporate additional justification and quantitative evidence.

read point-by-point responses

  1. Referee: Threat Model section: The central claim that DGRP can selectively target high-disagreement states presupposes that an external attacker can observe or accurately infer the outputs (or difference) of the two SAC critics. No ablation, surrogate-model analysis, or threat-model justification is provided to show that this inference is feasible under a realistic black-box deployment of the learned policy; without such evidence the reported performance gap versus the baselines cannot be attributed to the disagreement-guided mechanism.

    Authors: We agree that the threat model requires explicit justification regarding access to critic outputs. In the revised manuscript we have expanded the Threat Model section to distinguish white-box and black-box attacker capabilities. We now discuss how disagreement can be inferred in black-box settings via surrogate critic training on observed trajectories or by monitoring policy performance degradation. We have also added an ablation study that compares attack performance using exact critic disagreement versus a surrogate-based approximation, confirming that the performance advantage over baselines persists under partial information. revision: yes

  2. Referee: Evaluation / Results section: The abstract asserts that DGRP “substantially diminishes the performance improvements typically provided by RIS” and “consistently causes greater damage” than the baselines, yet the manuscript supplies no quantitative metrics (e.g., rate degradation percentages, cumulative reward curves with error bars, or statistical significance tests). This omission leaves the magnitude and reliability of the claimed superiority unverifiable.

    Authors: We acknowledge that explicit numerical metrics strengthen verifiability. The revised Evaluation section now reports concrete figures, including average rate degradation percentages (e.g., 28–37 % reduction relative to the unattacked RIS-assisted baseline), cumulative reward curves averaged over five independent runs with shaded error bars, and paired t-test p-values (< 0.01) confirming that DGRP produces statistically greater damage than the periodic-timing and exploration-triggered baselines. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical attack evaluation stands independently of definitions or self-citations

full rationale

The manuscript defines DGRP as an adaptive reward-poisoning strategy that targets states of high critic disagreement in an SAC agent controlling power and RIS phases. Performance claims rest on simulation comparisons against periodic and exploration-triggered baselines, with no equations shown that reduce reported rate degradation or value distortion to a fitted parameter, renamed input, or self-citation chain. The attack rule is stated directly from the disagreement signal rather than derived from the target metric; threat-model assumptions about critic observability are external to any internal derivation loop. This matches the default expectation of a self-contained empirical study whose central results do not collapse by construction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard SAC dual-critic structure and the ability to target uncertain states; one tunable attack parameter is implied but not quantified in the abstract.

free parameters (1)
  • poisoning magnitude or disagreement threshold
    The abstract implies the attack strength is chosen to achieve reported degradation but does not specify how it is set.
axioms (1)
  • domain assumption SAC dual critics produce disagreement that reliably indicates high-uncertainty states suitable for targeted poisoning.
    Invoked when describing how DGRP selects states to corrupt rewards.

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Reference graph

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