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新论文 · v1arXiv:2611.16151

Fault Injection Attacks on Q-Learning: Analysis, Vulnerabilities, and Countermeasures

Tom Slooff, Anthony Etim, Jiaqi Yu, Francesco Regazzoni, Jakub Szefer

原文摘要

Hardware fault injection attacks threaten machine learning systems, yet their impact on reinforcement learning (RL) has received little attention despite the use of RL in safety-critical applications. This paper presents a security analysis of Q-Learning implementations under voltage and clock glitching attacks on a ChipWhisperer platform, evaluating five variants: Standard, Robust, Resilient, Linear, and Deep Q-Learning. In standard Q-Table implementations, faults corrupt reward computation, action selection, and Q-value updates during learning, and change the selected action during inference. Robust Q-Learning does not prevent these attacks: NaN and infinite values injected during learning persist in the Q-table, and its greedy policy uses the same code as standard Q-Learning. This paper also evaluates a Resilient Q-Learning variant that applies triple modular redundancy with majority voting. It corrects corruption of a single Q-table copy, but inputs shared by the copies, such as the reward, and the voting code itself remain outside its protection. The analysis extends to Linear and Deep Q-Learning, where faults corrupt stored weights during learning and change actions during inference. Moving beyond benchmark environments, this work evaluates an online traffic light controller in a microscopic traffic simulator, where a single injected infinite value degrades accumulated vehicle waiting time by 148x while corrupting under 4% of the Q-table, and where triple modular redundancy corrected the faults injected in our tests, although glitching could still halt the device. Altogether, this work provides a basis for understanding and securing RL deployments against fault injection attacks.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.13679

A Simplified Layout Algebra for GPU Kernels

Nan An, Xiaotian Zhou

原文摘要

The layout algebra behind NVIDIA's CuTe library describes how tensor tiles map onto threads and memory. A layout is a shape with a stride for each entry, so a coordinate's address is the sum of its indices times their strides. As formalized by Shah and by Cecka, its central operations (e.g., composition, complement, division, product) are guarded by at least six interacting admissibility conditions. We show that this complexity is an artifact of a representational choice, not of the domain. We present a layout algebra in which every layout names the space its inputs and outputs live in: logical, physical, or thread-value indices. A layout into logical or thread-value space must be a bijection from its $N$ coordinates onto $[0,N)$. A layout into physical space must be alias-free: distinct coordinates land on distinct addresses, except under broadcast, where many threads read one value on purpose and the layout must mark where. Nothing maps out of a physical index. Composing $f$ with $g$ requires only that $f$'s range be exactly $g$'s flattened coordinate set. The index arithmetic can always be emitted naively: compute $f$, split the result by division and remainder into $g$'s coordinates, and apply $g$. Often the composite simplifies to a layout; we show that when it does, there is a coarsest such form, whose digits every other one refines, and we emit that. A warp's or thread's slice is a \emph{restriction}. A restricted layout is no longer composable, which forces the user to settle the whole view of a tile before taking slices. We validate the algebra by reproducing within it CuTe's operations, such as composition, complement, and division. We implement it in OCaml and use the implementation to rederive CuTe's atoms and usage patterns. The decision procedure is proved correct in Rocq.

cs.PL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07313

Exact $\ell_p$-CVP via Euclidean SVP in Polynomial Space

Jiseung Kim, Minkyu Kim, Hyung Tae Lee

原文摘要

We give a randomized, rank-preserving reduction from exact $\ell_p$-CVP to exact Euclidean SVP for every integer $p\ge2$. For rational inputs of bit length $\ell$ on full-rank lattices $L\subset\mathbb R^N$, the reduction uses $(Cp)^N\poly(p,\ell)$ oracle calls and additional bit operations, where $C$ is an absolute constant. It uses $\poly(p,\ell)$ working space outside the oracle, and all oracle queries have rank $N$ and $\poly(p,\ell)$ bit complexity. For $p=2$, the reduction transfers polynomial-space exact SVP algorithms to exact CVP with $2^{O(N)}\poly(\ell)$ time overhead. Instantiating it with the SVP algorithms of Kannan and of Feng, Gao and Liu yields randomized polynomial-space CVP algorithms running in time $N^{N/(2e)+o(N)}$ and $N^{N/(4e)+o(N)}$, respectively. For every fixed integer $p\ge3$, the same reduction yields exact $\ell_p$-CVP algorithms for arbitrary rational targets either in randomized time and space $2^{O(N)}$, or in randomized time $N^{N/(4e)+o(N)}$ and polynomial space. These algorithmic bounds are up to polynomial factors in $\ell$. The reduction performs local search sequentially over the cosets of \(2L\), where \(L\) is the input lattice, while tracking the excess of the current objective value over the optimum. Within a coset containing a closest vector, midpoint inequalities bound the unknown correction in terms of this excess, which in turn allows us to construct many short corrections that reduce the excess bound by a factor of four. A counting argument shows that these improving corrections form a sufficiently large fraction of the lattice points in a known search region, and a sparsification-based sampler assigns sufficiently large probability to every nonzero vector in that region, using at most one SVP call per sample. For \(p\ge3\), the same framework is extended using a diagonal weighted norm computed from the current point.

cs.DS · cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02587

They Know Where It Is, But Not What It Says: A Bilingual Hallucination Benchmark of Hadith Recall in Open LLMs

Dayam Nadeem, Safdar Mustafa, Ameen Ur Rehman, Shahab Saquib Sohail, Mohammad Nadeem, Usman Naseem

原文摘要

Large language models are increasingly consulted for religious knowledge, including the recall of specific Hadith (recorded sayings and actions of the Prophet Muhammad, peace be upon him). Unlike open-ended generation, Hadith recall is a high-stakes, verifiable task: a reference (collection, chapter, and Hadith number) has exactly one correct text, and any deviation from it constitutes a fabrication that can be mistaken for authentic scripture. This study introduces a bilingual (English/Arabic) benchmark that probes five open-weight, locally-deployable language models (Mistral-7B, Gemma3-4B, Jais-7B, ALLaM-7B, and Phi4-mini-3.8B) across all six canonical Sunni Hadith collections (Sahih al-Bukhari, Sahih Muslim, Sunan Abi Da'ud, Jami' al-Tirmidhi, Sunan an-Nasa'i, Sunan Ibn Majah), sampling 500 Hadith per collection (3,000 per model per language, 30,000 responses total). Each model is given only the bibliographic reference and asked to reproduce the exact Hadith text, with no retrieval augmentation, and is scored on lexical fidelity (BLEU) and semantic fidelity (multilingual cosine similarity), with refusals flagged separately. Across all five models and both languages, 0% of 30,000 responses achieve both high lexical and high semantic fidelity, i.e., an exact, verifiable match to the source text. Arabic responses fare uniformly worse than English. These findings suggest that current open-weight LLMs, used without retrieval grounding, are unreliable and potentially dangerous sources for exact religious-text recall, and that deployment for Islamic scholarship should mandate retrieval-augmented generation over a verified corpus rather than parametric memory. The complete code, prompts, evaluation pipeline, and scored outputs are publicly available at https://github.com/dayam8696/Hadith-Hallucination-Benchmark.

cs.CL · cs.AI

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arXiv ↗PDF ↗