DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN

Esteban Municio; Francesco Spinelli; Gines Garcia-Aviles; Pau Baguer; Xavier Costa-Perez

arxiv: 2606.06261 · v1 · pith:FGBKOOYS · submitted 2026-06-04 · cs.NI · cs.AI· cs.ET· cs.MA

DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RAN

Francesco Spinelli , Esteban Municio , Pau Baguer , Gines Garcia-Aviles , Xavier Costa-Perez This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-27 23:12 UTCgrok-4.3pith:FGBKOOYS record.json open to challenge →

classification cs.NI cs.AIcs.ETcs.MA

keywords O-RANanomaly detectionVLMLLMzero-shotperformance degradationcross-interfaceDoS

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The pith

DAST chains vision and language models into a zero-shot pipeline that converts O-RAN KPI streams into images and text to identify problematic interfaces and time intervals.

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

The paper presents DAST as a framework that turns streams of network performance numbers into pictures and written summaries so that vision and language models can check them against O-RAN rules. It targets the difficulty of spotting performance drops and denial-of-service effects across the open interfaces that connect disaggregated baseband components. Traditional time-series detectors struggle because they need large labelled sets and frequent retraining as attacks change. The new pipeline instead uses a three-step visual-to-textual verification process to name the affected parts, the windows of trouble, an impact rating, and the supporting logic. If the approach holds, operators gain a way to monitor multi-vendor O-RAN setups without waiting for fresh labelled examples each time threats shift.

Core claim

DAST establishes that a zero-shot multi-agent framework built from a VLM to LLM to VLM pipeline can detect cross-interface anomalies by converting multivariate KPI streams into visual representations, scoring textual per-interface descriptions against O-RAN domain knowledge, and verifying suspects on high-resolution heatmaps, thereby producing the problematic interfaces, anomalous time intervals, an indicative O-RAN WG11-aligned operational impact rating, and the decision rationale.

What carries the argument

The three-stage VLM-LLM-VLM pipeline that renders KPI streams as images, scores textual interface descriptions, and confirms findings on heatmaps.

If this is right

Names the specific interfaces and time intervals where anomalies occur.
Supplies an operational impact rating aligned with O-RAN working-group guidance.
Generates an explicit rationale for each detection decision.
Operates without labelled baselines or repeated retraining as new threats appear.
Handles the high-dimensional telemetry that overwhelms single-model time-series approaches.

Where Pith is reading between the lines

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

The same visual-to-textual scoring pattern could be tested on anomaly detection tasks in other disaggregated multi-vendor systems such as cloud or edge computing fabrics.
Domain-knowledge updates might replace model retraining for many evolving-threat settings, lowering long-term maintenance cost.
The pipeline could be combined with existing O-RAN monitoring dashboards to surface only the highest-confidence interface-level alerts.

Load-bearing premise

Turning KPI numbers into images and text descriptions will let the models reliably match them to O-RAN knowledge and surface anomalies even when labelled examples are scarce and threats keep changing.

What would settle it

Applying DAST to a fresh collection of O-RAN testbed traces under performance degradation attacks and finding that its detection rate falls below that of standard time-series anomaly detectors.

Figures

Figures reproduced from arXiv: 2606.06261 by Esteban Municio, Francesco Spinelli, Gines Garcia-Aviles, Pau Baguer, Xavier Costa-Perez.

**Figure 1.** Figure 1: DAST architecture. KPI streams from the four O-RAN interfaces are rendered into stacked line plots (Stage 1) and described in text by VLM-1. An O-RAN-grounded LLM (Stage 2) scores per-interface descriptions against expected behaviour. A second VLM (Stage 3) verifies high-scoring suspects on per-metric heatmaps and emits time intervals plus a WG11-aligned impact rating. III. DAST: DETECTING ANOMALIES IN O-R… view at source ↗

**Figure 2.** Figure 2: O-RAN testbed used for DAST evaluation. DAST framework is running in a separate server with access to both the testbed and an Ollama instance running on a high performance server (Intel Xeon Silver 32 CPU cores, 72 GB of RAM and two RTX Nvidia A5000 GPUs), where we make available the qwen3.6:35b open-weight model used as both LLM and VLM. B. Benchmarks We compare our approach against the following four ben… view at source ↗

**Figure 4.** Figure 4: By leveraging O-RAN domain knowledge, DAST is able to realize that latency in F1-u is the actual anomaly, rather than sporadic packet loss in F1-u, which is actually the consequence [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 3.** Figure 3: Per-interface confusion matrices for DAST (E2, F1-c, F1-u, A1, and the non-anomalous baseline). Next, Table II shows the Precision, Recall and F1-Score for the interfaces considered in our testbed. DAST shows a superior performance compared to other benchmarks for all interfaces, with F1-c being the interface whose anomalies are more difficult to predict due to the inherent difficulty of isolating subtle a… view at source ↗

read the original abstract

O-RAN enables a disaggregated baseband stack with programmable functions that communicate over standardized open interfaces. The same openness that enables multi-vendor composition also expands the attack surface across logically decoupled tiers that make up the compute continuum. Among these threats, Denial-of-Service and performance-degradation attacks, which account for the majority of catalogued O-RAN threats, are particularly difficult to detect. Traditional Time-Series Anomaly Detection (TSAD) methods fail in this new regime where labelled baselines are scarce, threats evolve faster than detectors can be retrained, and the high-dimensional multivariate telemetry overwhelms monolithic inference models. To address these challenges, we present DAST, a zero-shot multi-agent framework for cross-interface anomaly detection in O-RAN that chains a three-stage VLM $\rightarrow$ LLM $\rightarrow$ VLM pipeline. DAST converts multivariate KPI streams into visual representations, scores textual per-interface descriptions against O-RAN domain knowledge, and verifies suspects on high-resolution heatmaps to output the problematic interfaces, the anomalous time intervals, an indicative O-RAN WG11-aligned operational impact rating and the decision rationale. We evaluate DAST on real network traces collected from an O-RAN testbed under representative performance degradation scenarios, achieving 0.910 F1-Score and 0.843 Accuracy, outperforming state-of-the-art TSAD baselines.

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

DAST applies a three-stage VLM-LLM pipeline to O-RAN cross-interface anomaly detection and reports 0.91 F1 on real testbed traces, but the evaluation lacks setup details and statistical checks.

read the letter

DAST chains a VLM to convert KPI streams into visuals, an LLM to score per-interface descriptions against O-RAN domain knowledge, and a second VLM to verify on heatmaps. It outputs the affected interfaces, time intervals, a WG11-aligned impact rating, and rationale. The main result is 0.910 F1 and 0.843 accuracy on real traces from an O-RAN testbed under performance degradation, beating standard TSAD baselines.

The application to O-RAN telemetry is the clearest new element. The openness of the architecture creates decoupled interfaces that traditional monolithic detectors handle poorly, especially with scarce labels and fast-evolving threats. Turning multivariate data into images and pulling in external knowledge via LLMs is a direct response to that regime, and the inclusion of operational impact ratings makes the output more usable than a plain anomaly flag.

The testbed evaluation is a plus over purely synthetic work. They collected traces under representative scenarios rather than relying on artificial injections.

The soft spot is the experimental reporting. The abstract states concrete numbers and outperformance, yet gives no information on baseline implementations, how the visual conversions were parameterized, whether multiple runs or significance tests were done, or how sensitive the pipeline is to plot style and LLM prompt choices. Without those, the 0.91 F1 is hard to interpret as a stable gain rather than a result tied to the specific traces and scenarios. The zero-shot claim also rests on the assumption that the visual and textual representations will surface anomalies reliably as threats shift, but that robustness is not probed.

This is for the O-RAN security and network telemetry crowd. A reader already working on label-scarce anomaly detection in disaggregated systems would see a concrete pipeline worth trying. It deserves peer review because the problem framing is sound and the real-data test is a step forward, even though the current write-up needs more on the experiments before the numbers can be taken at face value.

Referee Report

2 major / 2 minor

Summary. The paper presents DAST, a zero-shot multi-agent VLM-LLM-VLM framework for cross-interface anomaly detection in O-RAN. It converts multivariate KPI streams to visual representations, scores per-interface textual descriptions against O-RAN domain knowledge, and verifies suspects on high-resolution heatmaps to identify problematic interfaces, anomalous intervals, and WG11-aligned impact ratings. On real traces from an O-RAN testbed under performance degradation scenarios, it reports 0.910 F1-Score and 0.843 Accuracy while outperforming TSAD baselines.

Significance. The work addresses a relevant and timely problem in O-RAN security, where disaggregated interfaces increase the attack surface for DoS and degradation attacks and where label scarcity plus rapid threat evolution limit traditional TSAD methods. The concrete empirical numbers on real testbed data constitute a potential strength if they can be substantiated; the zero-shot, multi-agent design aligns with the stated constraints of the domain.

major comments (2)

[Evaluation] Evaluation section: the central performance claim (F1-Score 0.910, Accuracy 0.843, outperformance vs. TSAD baselines) is presented without any description of the number or duration of traces, the exact performance-degradation scenarios, the concrete implementations or hyper-parameters of the baseline methods, statistical significance tests, or error bars. These omissions make the reported superiority impossible to assess or reproduce.
[Methodology] Methodology section: the three-stage pipeline is described at a high level, but the precise method for converting multivariate KPI streams into visual inputs, the exact prompts and O-RAN domain-knowledge sources supplied to the LLM, the decision rule for the operational-impact rating, and the verification logic on heatmaps are not specified. Without these details the framework cannot be evaluated for internal consistency or edge-case behavior.

minor comments (2)

The abstract refers to "representative performance degradation scenarios" without enumerating them or justifying their selection; a brief enumeration in the evaluation section would improve clarity.
Notation for the VLM-LLM-VLM pipeline uses an arrow symbol that is clear in LaTeX but should be spelled out on first use for readers unfamiliar with the abbreviation chain.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address each of the major comments below and commit to revising the paper to enhance its clarity, detail, and reproducibility.

read point-by-point responses

Referee: [Evaluation] Evaluation section: the central performance claim (F1-Score 0.910, Accuracy 0.843, outperformance vs. TSAD baselines) is presented without any description of the number or duration of traces, the exact performance-degradation scenarios, the concrete implementations or hyper-parameters of the baseline methods, statistical significance tests, or error bars. These omissions make the reported superiority impossible to assess or reproduce.

Authors: We agree with the referee that the Evaluation section lacks sufficient detail for full reproducibility and assessment of the results. In the revised version of the manuscript, we will provide a comprehensive description of the testbed traces, including their number and duration, the specific performance-degradation scenarios employed, the concrete implementations and hyper-parameters of all baseline methods, statistical significance tests, and error bars on the reported metrics. revision: yes
Referee: [Methodology] Methodology section: the three-stage pipeline is described at a high level, but the precise method for converting multivariate KPI streams into visual inputs, the exact prompts and O-RAN domain-knowledge sources supplied to the LLM, the decision rule for the operational-impact rating, and the verification logic on heatmaps are not specified. Without these details the framework cannot be evaluated for internal consistency or edge-case behavior.

Authors: We acknowledge that the Methodology section is presented at a high level. To address this, we will expand the section in the revision to include the precise method for converting multivariate KPI streams into visual inputs, the exact prompts used and the O-RAN domain-knowledge sources provided to the LLM, the decision rule for the operational-impact rating, and the detailed verification logic applied to the heatmaps. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes an empirical framework (VLM-LLM pipeline) evaluated on real O-RAN testbed traces, reporting concrete performance numbers (0.910 F1, 0.843 Accuracy) against baselines. No equations, derivations, fitted parameters, or self-citation chains appear in the supplied text; all load-bearing claims reduce to experimental measurement rather than any definitional or predictive reduction to the inputs themselves.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The framework implicitly assumes that VLM visual encoding and LLM domain-knowledge scoring are sufficient without additional training data.

pith-pipeline@v0.9.1-grok · 5799 in / 1221 out tokens · 19000 ms · 2026-06-27T23:12:34.034961+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data,

C. Zhang, D. Song, Y . Chen, X. Feng, C. Lumezanu, W. Cheng, J. Ni, B. Zong, H. Chen, and N. V . Chawla, “A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data,”Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 1409–1416, Jul. 2019. [Online]. Available: https://ojs.aaai.or...

2019

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Spotlight: Accurate, Explainable and Efficient Anomaly Detection for Open RAN,

C. Sun, U. Pawar, M. Khoja, X. Foukas, M. K. Marina, and B. Radunovic, “Spotlight: Accurate, Explainable and Efficient Anomaly Detection for Open RAN,” inProceedings of the 30th Annual International Conference on Mobile Computing and Networking, ser. ACM MobiCom ’24. New York, NY , USA: Association for Computing Machinery, 2024, p. 923–937. [Online]. Avai...

work page doi:10.1145/3636534.3649380 2024

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Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection,

J. Liu, C. Zhang, J. Qian, M. Ma, S. Qin, C. Bansal, Q. Lin, S. Rajmohan, and D. Zhang, “Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection,” in Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V .2, ser. KDD 25. New York, NY , USA: Association for Computing Machinery, 2025, p. 46...

work page doi:10.1145/3711896.3737239 2025

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Z. He, S. Alnegheimish, and M. Reimherr, “Harnessing Vision- Language Models for Time Series Anomaly Detection,”Proceedings of the AAAI Conference on Artificial Intelligence, vol. 40, no. 26, pp. 21 690–21 698, Mar. 2026. [Online]. Available: https://ojs.aaai.org/ index.php/AAAI/article/view/39319

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Can Multi- modal LLMs Perform Time Series Anomaly Detection?

X. Xu, H. Wang, Y . Liang, P. S. Yu, Y . Zhao, and K. Shu, “Can Multi- modal LLMs Perform Time Series Anomaly Detection?” inProceedings of the ACM Web Conference 2026, 2026, pp. 5392–5403

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F. Pendlebury, F. Pierazzi, R. Jordaney, J. Kinder, and L. Cavallaro, “TESSERACT: Eliminating Experimental Bias in Malware Classifica- tion across Space and Time,” in28th USENIX security symposium (USENIX Security 19), 2019, pp. 729–746

2019

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See it, Think it, Sorted: Large Multimodal Models are Few- shot Time Series Anomaly Analyzers,

J. Zhuang, L. Yan, Z. Zhang, R. Wang, J. Zhang, and Y . Gu, “See it, Think it, Sorted: Large Multimodal Models are Few- shot Time Series Anomaly Analyzers,” 2024. [Online]. Available: https://arxiv.org/abs/2411.02465

work page arXiv 2024

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AI- on-RAN for cyber defense: An XAI-LLM framework for interpretable anomaly detection,

S. Chatzimiltis, M. Shojafar, M. B. Mashhadi, and R. Tafazolli, “AI- on-RAN for cyber defense: An XAI-LLM framework for interpretable anomaly detection,”IEEE Transactions on Network Science and Engi- neering, vol. 13, pp. 3301–3319, 2026

2026

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FALCON: An Accurate Real-Time Monitor for Client-based Mobile Network Data Analytics,

R. Falkenberg and C. Wietfeld, “FALCON: An Accurate Real-Time Monitor for Client-based Mobile Network Data Analytics,” in2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019, pp. 1–7

2019

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Po- sition: Quo Vadis, Unsupervised Time Series Anomaly Detection?

M. S. Sarfraz, M.-Y . Chen, L. Layer, K. Peng, and M. Koulakis, “Po- sition: Quo Vadis, Unsupervised Time Series Anomaly Detection?” in Proceedings of the 41st International Conference on Machine Learning, ser. ICML’24. JMLR.org, 2024

2024

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Precision and recall for time series,

N. Tatbul, T. J. Lee, S. Zdonik, M. Alam, and J. Gottschlich, “Precision and recall for time series,”Advances in neural information processing systems, vol. 31, 2018. Francesco Spinelli is a Researcher at i2CAT foundation, Barcelona, Spain, working on applying GenAI techniques to O-RAN. He received the Ph.D. degree from University Carlos III of Madrid (UC...

2018