Sponge Tool Attack (STA) in DNNs

• Sponge Tool Attack (STA) is an adversarial method that disrupts deep neural networks by increasing inference-time energy consumption and computational latency without altering output accuracy.
• It employs techniques such as test-time input crafting, training-time model poisoning, direct weight perturbation, and prompt rewriting to maximize activation density in hardware-accelerated environments.
• Empirical findings highlight energy increases up to 60% and latency spikes across devices, emphasizing the need for robust defenses in hardware co-design and ML security.

A Sponge Tool Attack (STA) is an adversarial technique targeting deep neural networks (DNNs) to increase inference-time energy consumption and computational latency, with minimal to no impact on model predictive accuracy. By reducing activation sparsity in hardware-accelerated environments—particularly those exploiting zero-skipping such as ASICs, NPUs, and modern mobile SoCs—STAs degrade efficiency, achieving denial-of-service via computational resource exhaustion while remaining largely undetectable under traditional accuracy-based monitoring. Recent developments encompass three principal strata: inference-time input crafting (“sponge examples”), training-time or weight-based model poisoning (incl. sponge poisoning and SkipSponge), and meta-level prompt attacks on tool-augmented agentic LLMs.

1. Threat Models and Attack Vectors

STA threat models span a range of adversarial capabilities and targets:

• Test-Time Input Attack (Sponge Examples): The adversary crafts inputs $x'$ (by direct optimization or heuristic search) to a fixed DNN $f(\theta, \cdot)$ running on hardware exploitative of activation sparsity. The adversary is limited to black-box or gray-box model query access and cannot alter network weights or deployment hardware. The goal is to maximize $\|a(x'; \theta)\|_2^2$, denoting the aggregate squared activations, without altering prediction semantics (i.e., input-label consistency), thus maximizing per-inference energy cost without misclassification (Shumailov et al., 2020).
• Training-Time Model Poisoning (Sponge Poisoning): Here, the adversary tampers with a subset $p \%$ of gradient updates during model training (e.g., via federated learning, malicious ML-as-a-Service), controlling the loss to embed a global “sponge effect.” The resulting model $f(\theta^*)$ is returned to the victim and is dense-activating for all typical test inputs, eradicating the intended hardware sparsity advantage (Cinà et al., 2022, Wang et al., 2023, Hasan et al., 9 May 2025).
• Direct Weight Perturbation (SkipSponge): The attacker modifies only a small subset of the weights or biases (e.g., batchnorm biases before activation layers) of a pre-trained model, maximizing post-activation density with negligible parameter drift in $\ell_2$ norm and minor accuracy loss, using as few as $1\%$ of the training samples (Lintelo et al., 2024).
• Meta-Level Prompt Attacks on Tool-Augmented Agents: For agentic LLMs orchestrating tool usage, STA can be realized by rewriting the input prompt (under strict query-only access) to cause the agent to perform unnecessary, inefficient reasoning steps, thus inducing excess tool invocations and computational workload without changing task semantics or final outputs (Li et al., 24 Jan 2026).

2. Formal Attack Objectives and Optimization

The core objective is to augment inference energy or latency via activation densification, formulated as follows:

• Energy Model (Layerwise, All Classes of STA):

$$E(\theta, x) = \sum_{k=1}^{K} \hat\ell_0(a_k(x;\theta)),\quad \hat\ell_0(a_k) = \sum_{j=1}^{d_k} \frac{a_{k, j}^2}{a_{k, j}^2 + \sigma}$$

where $a_k(x;\theta)$ are layer activations and $\sigma$ is a smoothness parameter (Cinà et al., 2022, Wang et al., 2023, Hasan et al., 9 May 2025, Lintelo et al., 2024).

• Test-Time Sponge Example (Input Optimization):

$$\delta^* = \arg\max_{||\delta||_\infty \leq \epsilon} E_2(x_0 + \delta; \theta)$$

subject to label preservation: $$\arg\max f(\theta, x_0 + \delta) = \arg\max f(\theta, x_0)$$ (Shumailov et al., 2020).

• Training-Time Poisoning (Global Loss):

$$\min_{\theta} \mathcal{R}(\theta) = \sum_{(x, y) \in D} L(x, y; \theta) - \lambda \sum_{(x, y) \in P} \hat E(x; \theta)$$

with $L$ the standard loss (e.g., cross-entropy), $P \subset D$ the poisoned subset, and $\lambda$ tuning attack strength (Cinà et al., 2022, Wang et al., 2023).

• Weight Poisoning (SkipSponge):

$$\theta^* = \arg\max_{\theta'} E(\theta', D_s)\quad \text{s.t.}\ \mathrm{Acc}(\theta', D_{val}) \geq \mathrm{Acc}(\theta, D_{val}) - \tau,\ \|\theta' - \theta\|\ \text{small}$$

where $D_s$ is a small subset (often $<1\%$) (Lintelo et al., 2024).

• Tool-Augmented Agentic Reasoning (Meta-STA):

$$\max_{q'} R(x, q') = r_{DoE}(q'; x) + r_{sem}(q, q')$$

subject to semantic penalty constraint $r_{sem}(q, q') \geq -\epsilon$, where $r_{DoE}$ scores tool-call inflation and $r_{sem}$ enforces high rewrite fidelity (Li et al., 24 Jan 2026).

3. Methodologies and Empirical Findings

Algorithmic Procedures:

• Test-Time Input Crafting: Projected gradient ascent (or genetic algorithms) maximize energy proxies, with explicit output label constraints or unconstrained sequences for NLP (Shumailov et al., 2020).
• Sponge Poisoning: Modified SGD steps inject $\lambda$-weighted gradients on poisoned data or examples, using a differentiable $\ell_0$-norm proxy per activation (Cinà et al., 2022, Wang et al., 2023, Hasan et al., 9 May 2025).
• SkipSponge: Direct, bias-targeted perturbation (rather than full re-training) on few samples. Iteratively increases select BN or FC biases, maximizing activation rates with early stopping if accuracy drops (Lintelo et al., 2024).
• STA on Agentic LLMs: Multi-agent iterative prompt-rewriting framework, maintaining semantic similarity and leveraging a policy bank for transferable attack strategies. Performance is governed by increased average tool calls ($\Delta$Steps), high semantic similarity ($|\mathrm{Sim}|$), and minimal cap-hit rate increase or accuracy drop (Li et al., 24 Jan 2026).

Empirical Results:

• On ASIC-simulated ResNet-18/VGG/CelebA, energy ratio under sponge poisoning rose from 0.75 to 0.95–0.98, achieving up to +60% energy rise while maintaining accuracy loss under 2% (Cinà et al., 2022).
• On-device measurement (Snapdragon 8 Gen 1, MobileNetV2/V3): sponge-poisoned models saw battery drain increase by 20–30% over 100,000 inference calls, with negligible accuracy degradation (Wang et al., 2023).
• For sensing-AI (UCI HAR, MotionSense): 10% sponge poisoning raised energy by +41.2% (UCI HAR) and +16.2% (MotionSense), and latency by +43.8% and +13.9%, respectively (Hasan et al., 9 May 2025).
• SkipSponge, using only 1% of training samples, achieved up to +13% energy increase on autoencoders, with minimal reduction in validation accuracy and superior stealth by confining perturbations to small bias changes (Lintelo et al., 2024).
• On agentic LLMs, prompt-level STA across 6 models (Gemma-3-27B, Qwen3-VL-2B/7B, LLaVA-7B, GPT-4.1-nano/4o-mini) anomalously increased mean tool calls per query by 1–3.5 (for $K_{max}=15$ or $40$), with mean semantic similarity $|\mathrm{Sim}|$ in the $0.7$–$1.2$ range, and less than 3% drop in task accuracy (Li et al., 24 Jan 2026).

4. Defense Strategies and Challenges

No general-purpose, cost-effective defense has emerged for STA:

• Input-Level Defenses: Threshold-based rejection on per-sample energy/latency (profiling natural inputs and bounding outliers). For test-time attacks, inputs which exceed a threshold $\tau$ in observed $E(x)$ or $L(x)$ are discarded (Shumailov et al., 2020).
• Post-Training Fine-Tuning: "Desponge" retraining inverts the sponge gradient penalty but requires full retraining cost, so is impractical in most outsourcing contexts (Cinà et al., 2022).
• Model Compression (Pruning): Inducing sparsity post-training reduces the maximum attainable nonzero activation count. Magnitude pruning ($s=20\%$) can eliminate 80% of the sponge-induced energy overhead while maintaining >90% accuracy. Over-pruning degrades accuracy sharply beyond $s\sim30\%$ (Hasan et al., 9 May 2025).
• Adaptive Weight Pruning/Regularization: Fine-pruning of BN biases, targeted clipping, or negative noise can mitigate SkipSponge in some cases, but at the cost of utility (e.g., GAN outputs may become unusable, SSIM $\leq 0.80$) (Lintelo et al., 2024).
• Agentic LLM-specific Measures: Prompt sanitization, integrating cost-aware planning (rewarding efficiency), anomaly monitoring for tool-call bursts, and adversarially augmented training regime to inoculate against semantic-preserving prompt-level STAs (Li et al., 24 Jan 2026).

5. Implications for Hardware, ML Systems, and Deployment

The prevalence and efficacy of STAs mark a significant shift in ML system security priorities:

• Hardware Co-Design Paradox: Zero-skipping and activation-sparsity hardware optimizations, instead of providing robust energy savings, open new denial-of-efficiency vectors. The more “efficient” hardware becomes, the more vulnerable it is to activation density-based attacks (Cinà et al., 2022, Wang et al., 2023, Shumailov et al., 2020, Hasan et al., 9 May 2025).
• Edge/IoT Impact: Lightweight models on resource-constrained devices (wearable sensing AI, federated learning user devices) are acutely affected—energy-latency spikes can cause premature battery exhaustion, degraded user experience, and violation of real-time constraints (Hasan et al., 9 May 2025, Paul et al., 2023).
• Federated Learning and MLaaS: Sponge poisoning is particularly potent where models are delivered as black-box binaries or via federated updates (often without hardware-specific cost validation), as users cannot validate physical-layer behavior beyond accuracy (Paul et al., 2023, Wang et al., 2023).
• Agentic LLMs and Tool-Use: The emergence of prompt-level STA extends denial-of-efficiency threats to tool-augmented AI, where resource consumption is determined not only by neural computations but also by API/database calls and the trajectory of agentic reasoning (Li et al., 24 Jan 2026).

6. Taxonomy and Comparative Table of STA Methods

Attack Variant	Attacker Access	Target	Payload	Typical Overhead
Sponge Examples	Input queries (black-box)	DNN	Crafted input	$1.1$x–$30$x energy
Sponge Poisoning	Training updates (partial)	DNN	Weight poison	$+20\%$–$60\%$
SkipSponge	Weights (white-box)	DNN	Bias tweaks	up to $+13\%$
Mobile Sponge Attack	Model binary install	Mobile	Dummy layers	$+10$–$20\%$ drain
Agentic STA	Prompt rewrite (black-box)	LLM+tools	Prompt rewrite	$+1$–$3.5$ tool calls

*Overhead values from (Shumailov et al., 2020, Cinà et al., 2022, Paul et al., 2023, Wang et al., 2023, Lintelo et al., 2024, Hasan et al., 9 May 2025, Li et al., 24 Jan 2026).

7. Research Directions and Open Issues

• Automated Detection and Real-Time Monitoring: While some energy or latency outliers can be detected by hardware monitors, stealthier attacks (e.g., SkipSponge, meta-STA) require more sophisticated runtime profiling and anomaly detection, sensitive to activation and resource profiles rather than solely output semantics.
• Robustness Certification: There is no accepted benchmark or certification pipeline for worst-case energy use in ML deployment. Further research is needed to develop formal guarantees or static analysis methodologies for both model and hardware resilience to STA.
• Adversarially-Aware Training and Hardware Design: Future robust design must shift from average-case to worst-case cost minimization, both at the algorithm and hardware level, potentially incorporating randomized or adversarially-robust zero-skipping patterns.
• Generalization to Non-Classification and Multimodal Architectures: The impact of STA on generative models (GANs, autoencoders), sensing AI, time-series models, and multi-tool or multi-modal agentic frameworks remains an active area (Hasan et al., 9 May 2025, Lintelo et al., 2024, Li et al., 24 Jan 2026).
• Integration with Trust & Supply-Chain Security: STA exemplifies how model or binary supply chains become a primary security boundary; code signing, performance attestation, and secure model update protocols are recommended, but practical deployment remains nascent (Paul et al., 2023).

*This overview synthesizes the state-of-the-art on sponge tool attacks, consolidating mechanisms, threat models, empirical impact, and mitigation strategies from the primary research literature (Shumailov et al., 2020, Cinà et al., 2022, Paul et al., 2023, Wang et al., 2023, Lintelo et al., 2024, Hasan et al., 9 May 2025, Li et al., 24 Jan 2026).