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arxiv: 2606.02924 · v1 · pith:G2TEP4FU · submitted 2026-06-01 · cs.CV

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception

Mellon M. Zhang , Siddhant Panse , Zimo Fan , Akshal Dhal , Rishit Sarkar , Glen Chou This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-28 14:45 UTCgrok-4.3pith:G2TEP4FUrecord.jsonopen to challenge →

classification cs.CV
keywords LiDAR perceptionadversarial attacksrobustness evaluationpoint injectionpoint removalautonomous drivingblack-box attacks
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The pith

High-performing LiDAR models resist point removal better but prove more vulnerable to point injection attacks.

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

The paper introduces the ATLAS benchmark to test LiDAR perception models against simulated black-box sensor attacks that either add or remove points from real driving scans. It shows that models with stronger results on clean benchmarks handle removal attacks more effectively yet suffer greater failures under injection attacks than weaker models. The source of this pattern is traced to standard object database sampling used during training. This matters for autonomous driving because physical attacks on LiDAR can occur without any model access and current evaluation practices overlook these failure modes.

Core claim

Evaluating a broad set of current state-of-the-art LiDAR perception models with ATLAS reveals a robustness asymmetry in which stronger clean-data performers better withstand removal attacks yet are more vulnerable to injection attacks, with the vulnerability traced to object database sampling augmentations in training.

What carries the argument

The ATLAS benchmark that generates simulated point injection and point removal attacks across real driving sequences to measure model robustness under black-box conditions.

If this is right

  • Standard object database sampling during training creates architecture-agnostic failures against injection attacks.
  • The released generation code enables ongoing reproducible tests as new attack methods appear.
  • Both attack modes can be studied together to develop mitigations that address the observed asymmetry.

Where Pith is reading between the lines

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

  • Training pipelines may require new augmentation strategies that preserve removal resistance without increasing injection exposure.
  • Comparable evaluation suites for other sensors such as cameras could reveal whether similar performance-robustness trade-offs exist.
  • Clean benchmark scores alone may not reliably indicate resistance to physical sensor manipulations in practice.

Load-bearing premise

The simulated injection and removal attacks produce effects that match those of real physical black-box attacks on deployed LiDAR sensors.

What would settle it

A physical test in which real sensor attacks produce vulnerability patterns that differ from the asymmetry observed in the ATLAS simulations.

Figures

Figures reproduced from arXiv: 2606.02924 by Akshal Dhal, Glen Chou, Mellon M. Zhang, Rishit Sarkar, Siddhant Panse, Zimo Fan.

Figure 1
Figure 1. Figure 1: ATLAS fills a missing axis in LiDAR robustness evaluation. While existing benchmarks primarily study nat￾ural perturbations, ATLAS targets black￾box, sensor-level spoofing attacks. [20] demonstrated feasibility with 140 structured injected points, PLA-LiDAR [12] scaled to 4200 points with 51–98% false detection rates on PointPillars [15] and SECOND [27], PhantomLidar [13] injected 16,000 points via electro… view at source ↗
Figure 2
Figure 2. Figure 2: Atlas simulates the main attack modes. Point injection attacks inject spurious objects to [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Negative correlation between injection and removal robustness. Models with higher injection robustness tend to ex￾hibit lower removal robustness (R2 = 0.87). Each point rep￾resents a model; color encodes clean detection performance on the benchmark (higher is better). The dashed line shows the linear regression fit. Injection and re￾moval robustness ranks are com￾puted by averaging the per-setting ASR rank… view at source ↗
Figure 4
Figure 4. Figure 4: Decay of removal vulnerability. The attack success rate peaks when the history is fully saturated before decaying monotonically as clean observations enter the temporal memory buffer [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: High proportion of high-confidence phantom proposals. Across most models and point injection modes, at least 25% of false positives are high in confidence (>0.7). 6 Discussion In this section, we examine our most counterintuitive finding: models that perform better on clean benchmarks are often more vulnerable to point-injection attacks than weaker counterparts. We consider two possible explanations. First… view at source ↗
Figure 6
Figure 6. Figure 6: Decay of injection vulnerability. The ASR peaks at the end of the attack frames before decaying monotonically as clean frames enter the temporal memory buffer [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: False confidence during point injection. Detectors assign high confidence to injected phantom objects during the active attack frames, which rapidly dissipates once the temporal buffer is overwritten with uncompromised frames. As shown in [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Target confidence degradation and recovery. Point removal systematically suppresses real target confidence scores during the active attack, followed by a full recovery to baseline certainty as clean point clouds reconstruct the scene. In contrast, [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Systematic degradation of target confidence in removal attacks. Adversarial point removal effectively suppresses the certainty of legitimate detections, driving a significant proportion of target confidence scores below the 0.7 threshold. E LOT Implementation Details Our proposed Latent Occupancy Tracking (LOT) module operates in the polar occupancy domain to model temporal consistency of LiDAR observation… view at source ↗
read the original abstract

Autonomous driving perception is typically evaluated on clean benchmark data, yet real-world deployment requires robustness to rare, structured, and potentially adversarial sensor anomalies. This gap is especially critical for LiDAR, where external actors can physically manipulate the sensing process to induce black-box perception failures without accessing the model. Existing LiDAR benchmarks provide little visibility into this failure mode. Prior adversarial LiDAR studies have largely centered on attack hardware, geometric and algorithmic defenses, and early-generation detectors, leaving the robustness of modern perception systems unexplored. To address this evaluation gap, we introduce ATLAS (Adversarial Temporal LiDAR Attack Suite), the first large-scale, physically grounded evaluation benchmark for LiDAR perception models under black-box sensor attacks, simulating the two primary attack modes -- point injection and point removal -- across real driving sequences. Evaluating a broad cross-section of current state-of-the-art LiDAR perception models, ATLAS reveals a surprising robustness asymmetry: models with stronger performance on standard benchmarks tend to better withstand removal attacks, yet are actually more vulnerable to injection attacks than weaker models. We trace this vulnerability to standard object database sampling augmentations, revealing how current training practices can induce architecture-agnostic robustness failures, and study initial directions for mitigating both attack modes. We release the ATLAS generation code to support extensible, reproducible evaluations as attack capabilities evolve, helping make black-box sensor robustness an explicit consideration in future LiDAR perception development.

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 / 1 minor

Summary. The paper introduces ATLAS, a large-scale benchmark simulating physically grounded black-box point injection and removal attacks on LiDAR perception models across real driving sequences. It evaluates a range of state-of-the-art detectors, reports a robustness asymmetry (stronger clean-performance models resist removal attacks better but are more vulnerable to injection), attributes the injection vulnerability to standard object-database sampling augmentations, explores initial mitigations, and releases generation code for reproducibility.

Significance. If the simulated attacks faithfully reproduce the statistical and geometric effects of real physical LiDAR spoofing and occlusion, the benchmark would provide the first systematic evidence that current training practices induce architecture-agnostic robustness failures, directly informing safer LiDAR perception development.

major comments (2)
  1. [Methods (attack simulation)] Methods (attack simulation): the central robustness-asymmetry and augmentation-attribution claims rest on the unverified assumption that the point-injection and point-removal simulators produce point clouds whose intensity histograms, angular density, and temporal consistency match those inducible by physical black-box attacks on deployed hardware; no Kolmogorov–Smirnov tests, direct hardware comparisons, or ablation on simulator parameters are reported to support this fidelity.
  2. [Results (augmentation ablation)] Results (augmentation ablation): the claim that object-database sampling is the root cause of the injection vulnerability is load-bearing yet lacks a controlled experiment isolating this augmentation from other training choices (e.g., intensity scaling, point dropout) while holding architecture fixed; without such isolation the architecture-agnostic conclusion cannot be drawn.
minor comments (1)
  1. [Abstract and §1] Abstract and §1: the phrase “physically grounded” is used without a forward reference to the specific physical model or validation metric employed in the simulator.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which focus on the fidelity of the attack simulators and the strength of the augmentation attribution. We address each point below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: Methods (attack simulation): the central robustness-asymmetry and augmentation-attribution claims rest on the unverified assumption that the point-injection and point-removal simulators produce point clouds whose intensity histograms, angular density, and temporal consistency match those inducible by physical black-box attacks on deployed hardware; no Kolmogorov–Smirnov tests, direct hardware comparisons, or ablation on simulator parameters are reported to support this fidelity.

    Authors: The simulators are constructed from established physical models of LiDAR point injection (spoofing) and removal (occlusion) drawn from the existing literature on black-box sensor attacks. The reported robustness asymmetry and its link to training practices are empirical observations under these simulated conditions. We agree that additional validation would strengthen the presentation: the revised manuscript will include an ablation on simulator parameters (injection rate, intensity distribution, angular density) and Kolmogorov–Smirnov comparisons against published real-attack point-cloud statistics where such data exist. Direct hardware replication lies outside the scope of a simulation benchmark and is noted as future work. revision: partial

  2. Referee: Results (augmentation ablation): the claim that object-database sampling is the root cause of the injection vulnerability is load-bearing yet lacks a controlled experiment isolating this augmentation from other training choices (e.g., intensity scaling, point dropout) while holding architecture fixed; without such isolation the architecture-agnostic conclusion cannot be drawn.

    Authors: The current manuscript correlates the presence of object-database sampling with elevated injection vulnerability across multiple architectures. To isolate the effect, the revision will add a controlled ablation that holds architecture, optimizer, and all other augmentations fixed while toggling only object-database sampling. This experiment will directly support the architecture-agnostic attribution. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark with no derivations or self-referential predictions

full rationale

The paper is a large-scale empirical evaluation benchmark for LiDAR perception under simulated attacks. It reports observed performance asymmetries across models and attributes them to training augmentations based on direct evaluation results. No equations, fitted parameters renamed as predictions, self-definitional steps, or load-bearing self-citations appear in the abstract or described content. The central claims rest on external model evaluations and released simulation code rather than internal reductions. This matches the default case of a self-contained empirical study against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical benchmark paper; no free parameters, axioms, or invented entities are introduced or relied upon in the provided abstract.

pith-pipeline@v0.9.1-grok · 5796 in / 1029 out tokens · 26167 ms · 2026-06-28T14:45:46.149815+00:00 · methodology

discussion (0)

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Reference graph

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