Tootfinder

@… thank you, this probably explains <#FreeBSD

TIL that the new Google building in King's Cross is named "Platform 37" in reference to both the train station it sits next to, and a move played by AlphaGo.

AuthProbe: Specification-Driven, Multi-Identity Detection of Broken Object-Level Authorization in Recruitment API
Jay Barach
https://arxiv.org/abs/2607.20574 https://arxiv.org/pdf/2607.20574 https://arxiv.org/html/2607.20574
arXiv:2607.20574v1 Announce Type: new
Abstract: Broken Object-Level Authorization (BOLA), also known as Insecure Direct Object Reference (IDOR), has topped the OWASP API Security ranking since 2019 and is the root cause of some of the largest exposures of applicant data in recruitment technology. The defining feature of this flaw class is that a malicious request is byte-for-byte indistinguishable from a legitimate one, which is precisely why web application firewalls and single identity scanners fail to catch it. We present AuthProbe, an open-source, black-box scanner that detects BOLA and IDOR in HTTP APIs by driving its tests from an OpenAPI specification and by acting under two or more identities that the operator controls. AuthProbe discovers, for each identity, the objects that identity legitimately owns, then attempts to read one identity's objects while authenticated as another and confirms a leak by comparing the response against a ground-truth fetch by the true owner. It also walks predictable identifiers to expose enumeration and reports missing authentication and existence oracles. The tool returns a severity-thresholded exit code and machine-readable reports so that it can gate a continuous integration build. On a synthetic recruitment API in which the McHire failure class is reproduced, AuthProbe detects every planted cross-identity read with no false positives on a hardened counterpart, and its running time grows linearly with the number of objects under test. AuthProbe is released under the Apache 2.0 license with an authorized-use guardrail.
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Which Model Is Actually Serving You? IRIS: Budgeted Black-Box Auditing of Model Substitution and Routing Dilution in LLM Gateways
Yuewei Zhang, Zhi-Hai Zhang, Hanzhang Qin
https://arxiv.org/abs/2607.20860 https://arxiv.org/pdf/2607.20860 https://arxiv.org/html/2607.20860
arXiv:2607.20860v1 Announce Type: new
Abstract: Commercial LLM gateways mediate access to hosted models, but the served backend may not match the advertised one: it may substitute a cheaper model on every request or route only a fraction $\epsilon$ of requests to it. Prior black-box auditors often need a privileged signal (log-probabilities, token ranks, or reference samples) or a target-specific probe, fix the query budget in advance, and return a yes/no verdict. We present $\mathrm{IRIS}$, an audit that needs only the returned text: it asks endpoints to generate random numbers or strings, fingerprints the backend, and is the first to combine, in one text-only audit, detection of whole-stream substitution and fractional dilution, attribution of the served backend, routing-fraction ($\epsilon$) estimation, and a query budget it sizes itself. A cheap pilot fits the exponential query-error decay and freezes that budget before any suspect query is issued. On an intra-family Qwen3 ladder $\mathrm{IRIS}$ verifies the backend at $0.99$ AUROC and sharpens attribution as queries accumulate; across a commercial OpenRouter library it catches $\epsilon{=}0.3$ dilution on margin-qualified pairs at $0.85$ mean power ($0.017$ false-positive rate) and recovers $\epsilon$ to within $0.04$ for enrolled diluents; and a live cross-provider audit flags $14$ of $15$ same-model provider pairs by genuine quantization and kernel deviations, corroborated on third-party MET traces. Against comparable black-box auditors, $\mathrm{IRIS}$ matches or beats detection on shared tasks, and adaptive allocation lifts the matched-budget target-hit rate from $73$% to $87$%. Further experiments cover adversarial gateways, knob identifiability, unseen diluents, and false-positive control.
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Deckard: She's a replicant, isn't she?
Tyrell: I'm impressed. How many questions does it usually take to spot them?
Deckard: I don't get it, Tyrell.
Tyrell: How many questions?
Deckard: Twenty, thirty, cross-referenced.

Packet-Loss Robust 3D Gaussian Compression via Atomic Packaging and GNN-based Error Concealment
Yuxuan Tao, Xuerui Ma, Hao Zhang, Chunhua Peng
https://arxiv.org/abs/2607.17916 https://arxiv.org/pdf/2607.17916 https://arxiv.org/html/2607.17916
arXiv:2607.17916v1 Announce Type: new
Abstract: 3D Gaussian Splatting (3DGS) and recent compression schemes such as HAC enable high-fidelity real-time neural rendering, but their bitstreams are fragile under packet loss during network streaming. Existing compression methods often separate correlated anchor attributes into independent streams, so losing one packet can create attribute-inconsistent broken anchors and severe rendering artifacts. We propose a packet-loss robust 3DGS transmission and error concealment framework. On the encoder side, anchor-level atomic packaging jointly encapsulates all attributes of each anchor, converting corrupted-attribute failures into clean missing-anchor erasures. Stratified random grouping further disperses packet losses across the spatial domain to avoid large contiguous voids. On the decoder side, we formulate recovery as prior-aware attribute inpainting. A Context-Aware Residual Interpolation (CARI) branch uses hash-grid prior predictions and neighboring residuals to build a robust baseline, while a lightweight two-layer graph neural network with cross-attention over hash-grid priors refines high-frequency attribute residuals. Attribute-wise confidence control falls back to interpolation when learned predictions are unreliable. Experiments under 20 percent random packet loss on BungeeNeRF, Mip-NeRF 360, and Tanks and Temples show that the proposed method substantially improves over no-concealment transmission and limits average PSNR degradation to about 3 dB relative to the lossless HAC reference.
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