Gradient-Data-free Bit-Flip Attack

• The paper presents a gradient-data-free method that selectively flips key weight bits to drastically reduce DNN accuracy without using training data or gradients.
• It details algorithmic frameworks including magnitude-based ranking and synthetic data-driven gradients to identify and exploit vulnerable bit positions in various model architectures.
• Empirical results show significant performance drops across diverse models, highlighting the urgency for robust defenses like bit protection and hardware-level safeguards.

A gradient-data-free bit-flip attack is an adversarial technique that disrupts deep neural network (DNN) performance by strategically flipping a minimal set of weight or code bits, without requiring access to training data or parameter gradients. These attacks leverage model weights, architectural features, or public activation statistics to identify vulnerable bit positions such that their alteration leads to catastrophic drops in accuracy or functionality. Gradient-data-free methods expand traditional bit-flip attack (BFA) capabilities beyond white-box and data-dependent settings, enabling practical exploitation of deployed models in diverse computational environments, including quantized networks, LLMs, and compiled DNN executables (Galil et al., 11 Feb 2025, Almalky et al., 27 Nov 2025, Ghavami et al., 2021, Chen et al., 2023).

1. Threat Models and Attack Objectives

Gradient-data-free bit-flip attacks are formulated under restricted threat models that exclude access to model gradients or real input data. Attack objectives typically fall into two categories:

• Untargeted Failure: Reduce model accuracy to random-guess levels or significantly degrade generation quality.
• Targeted Misbehavior: Induce the model to output specific (incorrect) responses or predictions at high frequency.

Attackers are assumed to have white-box access to network architecture and weights, occasionally restricting themselves to publicly recoverable code or activation statistics. Hardware-level exploits such as RowHammer are often used to implement bit-flip injection, providing physical leverage over bit reliability in DRAM (Chen et al., 2023, Almalky et al., 27 Nov 2025).

2. Algorithmic Frameworks and Saliency Metrics

Several distinct algorithmic strategies are used to identify and exploit vulnerable bit positions without data or gradient information:

• Magnitude-Based Ranking (“Deep Neural Lesion”): For floating-point DNNs, flipping the sign bit of the largest-magnitude weights yields maximal disruption. Formally, the saliency score is $S(\theta_i) = |\theta_i|$, with selection often limited to one per kernel to prevent cancellation effects. This "pass-free" approach entirely foregoes forward/backward computation and is effective on a wide array of architectures (Galil et al., 11 Feb 2025).
• Activation-Driven Vulnerability Indices (“Ghosting Your LLM”): In large-scale LLMs, vulnerable bits are located by identifying layers with the largest shifts in activation standard deviation, denoted $$\Delta\sigma_\ell = |\sigma(h_\ell) - \sigma(h_{\ell-1})|$$, and then ranking weights within that layer by $|W_{ij}| \cdot \|A_j\|_2$, where $A_j$ is the activation vector for the $j^\text{th}$ input channel. No gradients are required; only public or synthetic data is used to propagate activations (Almalky et al., 27 Nov 2025).
• Synthetic-Data-Driven Gradients (“Blind Data Adversarial Bit-flip Attack”): BDFA constructs a synthetic batch by matching internal batch normalization statistics and arbitrary label assignments. Once the synthetic dataset is fixed, standard gradient-based BFA techniques are used to rank and select bits although these gradients are computed only on the synthetic batch (Ghavami et al., 2021).

3. Implementation: Bit Selection and Flipping

The identification and execution of bit flips in gradient-data-free attacks can be summarized as follows:

• For floating-point (FP32/FP16) networks, focus is placed on the sign bit of large-magnitude weights. In more computationally permissive scenarios, a proxy loss on random inputs further amplifies disruption by incorporating hybrid magnitude-gradient heuristics (Galil et al., 11 Feb 2025).
• For quantized networks or LLMs, emphasis shifts to weights associated with high-activation channels or those within outlier-driven layers, exploiting structural or activation-based saliency criteria (Almalky et al., 27 Nov 2025).
• Flips are injected directly into model memory—either by software manipulation, hardware exploits (e.g., RowHammer, DMA), or code corruption of DNN executables—without requiring iterative optimization or access to confidential weights (Chen et al., 2023).

4. Empirical Effectiveness and Comparative Results

Empirical studies reveal that gradient-data-free approaches can match or exceed the efficacy of data/gradient-dependent methods, often with dramatically reduced computational overhead:

Attack Variant	Data Needed	Typical Flips	Effect on ResNet50/ImageNet
Pass-Free DNL	None	2–10	AR(2)=99.8%, AR(10)=52%
Single-Pass DNL (1P-DNL)	None/Random	2	AR(2)=99.8%
GDF-BFA (LLMs)	Public only	1–3 (INT8)	PPL jump 12→1,493 (Llama-2-7B, WikiText-2)
BDFA (Synthetic batch)	Synthetic	4	Acc 75.96%→13.94% (ResNet50, CIFAR-100)
Prior BFA (with data, grads)	Data, Gradients	11–23	AR≅99.7% (ResNet50); 75% (DeepHammer)

AR = Accuracy Reduction; PPL = Perplexity; (Galil et al., 11 Feb 2025, Almalky et al., 27 Nov 2025, Ghavami et al., 2021)

Efficiency statistics indicate O(|θ|) time for DNL, only O(k) storage, and constant per-task complexity for LVI/WVI-based attacks in LLMs.

5. Attack Surfaces: Compiled DNNs and Transferability

The vulnerability of DNN executables compiled by frameworks such as TVM and Glow expands the BFA surface beyond weights alone. In this context, attackers can exploit bits in the compiled .text section representing model structure rather than specific weights. A systematic search identifies "superbits"—bits whose flipping reliably degrades model accuracy across multiple surrogates trained with different (randomized) weights. Pervasive and transferable vulnerabilities are observed: single-bit flips in .text can drop accuracy in models such as ResNet50 from 91.3% to 10.0%, and more than 16,000 vulnerable bits may exist per executable (Chen et al., 2023).

6. Defenses and Limitations

Countermeasures specific to gradient-data-free bit-flip attacks include:

• Selective Bit Protection: Replicating or ECC encoding the most empirically vulnerable sign/MSB bits yields robust defenses; e.g., protecting top-5% sign bits reduces AR(100,000) from ~80% to <10% (Galil et al., 11 Feb 2025).
• Code Obfuscation and Randomization: At the executable level, disrupting memory page equivalence or randomizing instruction layouts thwarts exploit propagation via shared memory mechanisms (e.g., KSM/TPS) (Chen et al., 2023).
• Layer/Weight Masking: Random masking, quantization noise injection, or dynamically reordering layers can help mitigate attack reliability, though may incur performance penalties on clean data (Almalky et al., 27 Nov 2025).
• Hardware-Level Protections: DRAM error correction, page scrubbing, and increased refresh rates can block RowHammer-style exploits.

Limitations include the challenge of achieving highly targeted attacks without any data, and, for certain methods (e.g., BDFA), the requirement for synthetic batch construction which can introduce computational overhead.

7. Significance and Open Challenges

Gradient-data-free bit-flip attacks establish that catastrophic model failures can be orchestrated in settings lacking data and gradient information, demonstrating intrinsic structural and representation-level fragilities in large-scale DNNs and LLMs. With only a handful of carefully selected bit flips—often identifiable through simple metrics—models ranging from vision classifiers to billion-parameter LLMs can be reduced to random-guessing performance (Galil et al., 11 Feb 2025, Almalky et al., 27 Nov 2025, Ghavami et al., 2021). This suggests the need for both architectural and deployment-level changes, such as integrating security validation into toolchains and prioritizing fine-grained protection of highly sensitive weight and code regions (Chen et al., 2023). The stability of vulnerability indices across datasets and tasks points to a structural universality of these weaknesses, implying future defense efforts must account for dynamic, data-independent exploitability.