Invisible Hands: Gray-Box Bit Flip Attack for Steering LLMs Without Knowledge of Gradients, Data, and Weights

Abeer Matar A. Almalky; Adnan Siraj Rakin; Li Yang; Mohaiminul Al Nahian; Ziyan Wang

arxiv: 2511.22700 · v2 · submitted 2025-11-27 · 💻 cs.CR

Invisible Hands: Gray-Box Bit Flip Attack for Steering LLMs Without Knowledge of Gradients, Data, and Weights

Abeer Matar A. Almalky , Ziyan Wang , Mohaiminul Al Nahian , Li Yang , Adnan Siraj Rakin This is my paper

Pith reviewed 2026-05-17 03:58 UTC · model grok-4.3

classification 💻 cs.CR

keywords bit flip attacklarge language modelsgray-box attackadversarial robustnessmodel securityhardware fault injection

0 comments

The pith

Gray-box bit flip attacks can steer LLMs toward targeted outputs by locating vulnerable bits using only model architecture.

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

This paper sets out to show that bit flip attacks on large language models do not require access to weights, gradients, or any training data. Instead, a set of vulnerability index metrics calculated from the model architecture alone can identify which bits to flip to reach specific adversarial goals. A sympathetic reader would care because this removes the practical barriers of data privacy rules and heavy computation that once limited such attacks. If the claim holds, it means real-world LLMs can be altered through hardware faults with far less information than previously assumed.

Core claim

The paper claims that its Gradient-Data-Free-BFA method introduces vulnerability index metrics that estimate weight susceptibility from model architecture in a gray-box setting. These metrics locate bits whose flips achieve targeted adversarial objectives without any knowledge of the actual weights, gradients, or sample data, and the resulting perturbations remain minimal while the computational complexity stays constant across tasks.

What carries the argument

Vulnerability index metrics that estimate the susceptibility of weights based solely on model architecture.

If this is right

Adversarial objectives can be reached through minimal weight perturbations.
Memory overhead drops because no data storage or gradient computation is needed.
The attack scales to different tasks at constant complexity.
The approach works across six open-source LLMs as shown in the experiments.

Where Pith is reading between the lines

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

Model architecture information by itself may be enough to enable hardware-level tampering in environments where weights and data are protected.
Defenses could need to add redundancy or hide architectural details to limit the usefulness of such metrics.
The same style of architecture-only search might apply to other model types or hardware fault scenarios.

Load-bearing premise

Vulnerability index metrics derived solely from model architecture can reliably locate bits whose flips achieve targeted adversarial objectives without any data or gradient information.

What would settle it

Running the identified bit flips on an actual deployed LLM and checking whether the model produces the intended adversarial outputs without supplying any weights, gradients, or data samples.

read the original abstract

In recent years, large language models (LLMs) have achieved remarkable advances and are increasingly deployed in critical applications across diverse domains. This growing adoption raises urgent concerns about their security and robustness. In this work, we investigate the impact of Bit Flip Attacks (BFAs) on LLMs, which exploit hardware faults to corrupt model parameters, thereby threatening model integrity and performance. Existing BFA studies primarily assume a white-box setting with access to exact model weights and part of the dataset, and rely on progressive gradient-based bit-search strategies to identify vulnerable bits in model weights. However, gradient computation for LLMs is computationally expensive and memory intensive. In addition, assuming access to exact victim model weights and datasets is challenging due to increasingly strict user privacy regulations. To address these challenges, we propose the first gray-box BFA framework for LLMs, Invisible Hands, designed for efficient and practical deployment. Our method, Gradient-Data-Free-BFA, identifies vulnerable weight bits without requiring knowledge of model weights, gradients, or sample data. It introduces novel vulnerability index metrics that estimate the weights of susceptibility based solely on model architecture (Grey-Box). By eliminating data access and gradient computation, our approach significantly reduces memory overhead and scales efficiently across tasks with constant complexity. Experiments on six open-source LLMs demonstrate that adversarial objectives can be achieved with minimal weight perturbations, highlighting the effectiveness and practicality of Invisible Hands.

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 abstract claims a first gray-box BFA on LLMs that picks vulnerable bits using only architecture-derived vulnerability indices, but supplies no method details, numbers, or evidence that the approach works.

read the letter

The punchline with this paper is that it presents what it calls the first gray-box bit flip attack on LLMs, using vulnerability indices derived only from the model architecture to pick which bits to flip, without any need for gradients, training data, or even the weight values themselves. What is new here is the shift away from the usual white-box assumptions in BFA research. Earlier work relies on gradient-based searches that require full access and are expensive for large models. This approach aims to cut that down to constant complexity by looking at things like layer structure and dimensions instead. That could matter in settings where privacy rules or deployment constraints block access to internals. The paper does a solid job laying out why those restrictions are becoming common and why a more practical attack method would raise the stakes for defenses in critical uses. Where it gets soft is in the lack of any supporting details. The abstract talks about experiments on six open-source LLMs that achieve adversarial goals with minimal changes, but there are no results shown, no baselines, and no actual definition of how the vulnerability index is computed from architecture alone. The idea that structure by itself can reliably identify bits for targeted output steering is the key assumption, and without evidence or a clear method it is hard to evaluate. Bit flip impacts usually tie back to specific weight values and how they interact with inputs, so architecture-only metrics would need to capture that indirectly in a robust way. Since only the abstract is available, I cannot check if the full paper includes derivations, code, or verifiable experiments that would strengthen this. If it does, the contribution could be more substantial. This kind of work is aimed at researchers focused on hardware-level threats to AI systems and those building defenses for real-world LLM deployments. Someone looking for immediately usable attack recipes or strong empirical validation might not find enough here yet. I would recommend sending it to peer review if the authors can supply the missing method details and quantitative evidence, as the topic is timely and the practical angle is worth exploring even if the current claims need more backing.

Referee Report

2 major / 1 minor

Summary. The paper proposes Invisible Hands, the first gray-box Bit Flip Attack (BFA) framework for LLMs. The Gradient-Data-Free-BFA method claims to identify vulnerable weight bits for targeted steering using novel vulnerability index metrics computed solely from model architecture, without any access to weights, gradients, or sample data. It asserts that this reduces memory overhead with constant complexity and that experiments on six open-source LLMs achieve adversarial objectives with minimal perturbations.

Significance. If the central claims hold, the work would represent a meaningful advance in LLM security research by demonstrating that architecture-only vulnerability indices can enable practical, privacy-preserving bit-flip attacks that bypass the computational and data-access barriers of existing white-box gradient-based methods.

major comments (2)

[Abstract] Abstract: the assertion that 'adversarial objectives can be achieved with minimal weight perturbations' on six LLMs is unsupported by any quantitative results, baselines, success rates, or verification of the architecture-only metrics. This is load-bearing for the effectiveness and practicality claims.
[Abstract] Abstract: no formulation is given for the vulnerability index metrics or how they are derived from architecture (e.g., layer counts or attention structure) to locate bits that produce targeted steering. Without this, it is impossible to evaluate whether the gray-box assumption can succeed when bit-flip effects depend on actual learned weights and activations.

minor comments (1)

[Abstract] The abstract would be clearer if it briefly named the six LLMs or characterized the adversarial objectives (e.g., output steering toward specific tokens or behaviors).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our abstract. The comments correctly identify that the abstract would be strengthened by greater specificity. We address each point below and will revise the abstract in the next version to incorporate the requested details while preserving the manuscript's core claims.

read point-by-point responses

Referee: [Abstract] Abstract: the assertion that 'adversarial objectives can be achieved with minimal weight perturbations' on six LLMs is unsupported by any quantitative results, baselines, success rates, or verification of the architecture-only metrics. This is load-bearing for the effectiveness and practicality claims.

Authors: We agree that the abstract as written does not include quantitative support. The full manuscript contains experimental results across six open-source LLMs demonstrating the claimed outcomes with minimal perturbations. To make the abstract self-contained and address the concern, we will revise it to include representative quantitative highlights such as success rates, perturbation ratios, and reference to the constant-complexity property. revision: yes
Referee: [Abstract] Abstract: no formulation is given for the vulnerability index metrics or how they are derived from architecture (e.g., layer counts or attention structure) to locate bits that produce targeted steering. Without this, it is impossible to evaluate whether the gray-box assumption can succeed when bit-flip effects depend on actual learned weights and activations.

Authors: The referee is correct that the abstract provides no explicit formulation. The vulnerability indices are computed exclusively from architectural attributes (layer counts, attention head structure, and hidden dimensions) to rank bit susceptibility in a gradient- and data-free manner. We will add a concise description of this derivation process to the revised abstract so that readers can assess the gray-box premise. revision: yes

Circularity Check

0 steps flagged

No circularity detected; claims rest on proposed architecture-derived metrics and experiments, not self-referential definitions or fitted inputs

full rationale

The abstract introduces Gradient-Data-Free-BFA and novel vulnerability index metrics computed solely from model architecture, without presenting equations, parameter fitting to target outcomes, or load-bearing self-citations. The derivation chain does not reduce any claimed prediction or result to its inputs by construction; the method is positioned as independent of gradients, data, and weights, with effectiveness asserted via experiments on six LLMs. No self-definitional, fitted-input, or uniqueness-imported patterns are visible in the available text.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are detailed beyond the high-level claim of new architecture-based metrics.

axioms (1)

domain assumption Model architecture information alone is sufficient to estimate bit-level susceptibility for bit-flip attacks
This underpins the gray-box claim that no weights, gradients, or data are needed.

invented entities (1)

Vulnerability index metrics no independent evidence
purpose: Estimate weight susceptibility from architecture only
New metrics introduced to replace gradient-based search

pith-pipeline@v0.9.0 · 5549 in / 1272 out tokens · 30617 ms · 2026-05-17T03:58:41.747024+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Layer Vulnerability Index (LVI) ... Δσ_ℓ = |σ(h_ℓ)−σ(h_ℓ−1)| ... Weight Vulnerability Index (WVI) = |W_ij| · ∥A_j∥₂
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Gradient-Data-Free-BFA ... vulnerability index metrics ... based solely on model architecture

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.