KEVWire

Security intelligence, with context.

Response snapshot Oct 29, 2026, 02:31 UTC

Back to the wire
Permalink
VulnerabilityCVE-2026-115913

vLLM: Phi-4 multimodal audio encoder materializes and retains full attention matrices: a single max-length audio OOM-crashes the vLLM engine

The Phi-4 multimodal audio encoder (phi4mm_audio.py / phi4mm_utils.py) runs its conformer attention in eager mode by default: the official HF config of microsoft/Phi-4-multimodal-instruct does not set use_pt_scaled_dot_product_attention, so the default False applies (phi4mm_audio.

At a glance

Exploited
Not known
Severity
6.5 Medium (CVSS 3.1)
Chance of exploitation
0.3% in the next 30 days
Sources
1 source

What to do

GitHub lists first patched versions: vllm: 0.32.0. Confirm the affected range and upgrade guidance in the exact advisory.

Read GitHub-reviewed advisory GHSA-8397-8qf6-f2gm (opens a new tab)

What happened

Summary

The Phi-4 multimodal audio encoder (phi4mm_audio.py / phi4mm_utils.py) runs its conformer attention in eager mode by default: the official HF config of microsoft/Phi-4-multimodal-instruct does not set use_pt_scaled_dot_product_attention, so the default False applies (phi4mm_audio.py:166, :876; phi4mm_utils.py:1669).

The eager path fully materializes the attention probability matrix of shape [n_chunks, 16, 500, 500] per layer — and then stores it on the module: self.attn = attn (phi4mm_utils.py:1802, a torch.jit.Attribute declared at :1693), keeping a live reference for every one of the 24 encoder layers.

The only gates on audio length are the server-side limits VLLM_MAX_AUDIO_CLIP_FILESIZE_MB=25 and VLLM_MAX_AUDIO_DECODE_DURATION_S=600 (envs.py:83-84) — i.e. a 10-minute clip is a legal input by default. A 600 s clip becomes ~60,000 mel frames, which the encoder unfolds into 500-frame chunks (phi4mm_audio.py:996, max_seq_len = 500), giving n_chunks ≈ 120.

Continue reading

Each layer therefore materializes a 120×16×500×500 matrix (~1–2 GiB depending on dtype), and the module attribute keeps all completed layers' matrices alive, so GPU memory climbs monotonically within a single request until an allocation fails.

Verified end-to-end on the official vllm/vllm-openai:v0.31.0 image: one 19.2 MB silent 600 s WAV → worker torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.29 GiB ... 501.75 MiB free ... 22.09 GiB allocated by PyTorch inside get_audio_features (phi4mm_audio.py:1279 → :1332 → phi4mm_utils.py:1386) → EngineCore encountered a fatal error → API EngineDeadError → whole server down; /health refuses connections. Total time from request to dead service: ~6 seconds.

A secondary effect: because self.attn persists on the module after the request ends, the matrices of the last audio request stay resident indefinitely (observed +50 MiB pinned after a single 5 s control request); they are only overwritten by the next audio request, so the resident footprint equals the largest audio processed since startup.

Details

1. Root cause. MultiHeadedAttention.forward_attention in eager mode computes attn = masked_softmax(scores, mask) and unconditionally executes self.attn = attn (phi4mm_utils.py:1795-1802). The attribute is a leftover inference/debug hook (used by torch.jit.

Attribute(None, Tensor | None) at :1693); in serving it serves no purpose but pins the full [batch·chunks, heads, T, T] tensor per layer past both the layer's forward and the request's lifetime. The SDP attention branch (use_pt_scaled_dot_product_attention=True) never materializes this matrix — but the HF config does not enable it and the code default is False.

2. Why one request suffices. The conformer chunks long audio to max_seq_len = 500 frames (phi4mm_audio.py:996). Duration is bounded only by VLLM_MAX_AUDIO_DECODE_DURATION_S=600, so n_chunks reaches ~120 for a maximal clip. With 16 heads and fp32 scores, one layer's attention tensor is ~1.8 GiB (the observed failing allocation was 2.29 GiB, consistent with the scores+bias materialization).

The encoder has num_blocks=24, attention_heads=16, attention_dim=1024, time_reduction=8 (HF config). Weights plus a handful of retained per-layer matrices exhaust the ~2.3 GiB headroom that --gpu-memory-utilization 0.9 leaves on a 24 GB card — mid-forward, on the first request.

4. Deployment reality. --trust-remote-code is the documented, standard way to serve this model (the HF model card instructs it), so the finding applies to default deployments. The crash is deterministic, not load-dependent: scheduler stats at death show a single running request and near-zero KV cache usage.

Impact

Any unauthenticated client that can reach the chat/completions API of a default deployment serving Phi-4-multimodal-instruct (or any model reusing this audio tower) can kill the entire engine with one request carrying a perfectly legal 10-minute audio clip (silent noise suffices — no crafted content needed). Full denial of service, requiring a process restart. On GPUs with more headroom (e.g.

80 GB at default utilization) a single request may survive, but the eager path still materializes GiB-scale per-layer matrices and retains the last request's tensors indefinitely, so a few sequential max-length requests — or normal traffic following one — achieve the same result.

Relation to prior art: the only public Phi-4-mm advisory we found, GHSA-vc6m-hm49-g9qg, concerns a CPU-side quadratic-cost path — different subsystem, different mechanism. GitHub search for "phi4mm audio OOM/memory" (31 results, 2026-10-08) shows no report of this attention-retention issue; main (2026-10-08) is unchanged.

Suggested remediation (any one breaks the chain; 1+2 recommended):

1. Remove self.attn = attn from the serving path (or gate it behind an explicit debug/output_attentions flag) so attention matrices are freed when the layer returns. 2. Default use_pt_scaled_dot_product_attention to True (flash/SDPA kernels never materialize the [B, H, T, T] matrix); keep eager only as an opt-in. 3. Additionally bound the encoder input (frames or duration) for this model so the eager path cannot reach GiB-scale single-layer allocations even if enabled.

This advisory was fixed in [MR60614](https://github.com/vllm-project/vllm/pull/60614).

Affected package ranges reported by GitHub: vllm: < 0.32.0

Scores and how they were measured

CVSS base severity

6.5

Version 3.1As of

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Measurement source (opens a new tab)

How bad it is if exploited. Not a prediction of exploitation or proof you are exposed.

EPSS probability

0.3%

Scored Percentile 23.2%

Model and measurement source (opens a new tab)

A model's estimate of exploitation in the next 30 days, not certainty or your own risk.

Related technology

Sources

  1. Primary source

    GitHub-reviewed advisory GHSA-8397-8qf6-f2gm (opens a new tab)

    The public advisory supplies the description, affected package/version ranges, and any listed CVSS metric. GitHub review is not local review.

    Published Checked

  2. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-8397-8qf6-f2gm, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

  3. Primary source

    FIRST measured EPSS score (opens a new tab)

    Measured EPSS probability and percentile for CVE-2026-115913, scored 2026-10-28. This is a model estimate, not proof of exposure.

    Published Checked

Cite this record

For factual claims, cite the primary sources above; cite KEVWire for its compilation, dates, and classifications.

APA

KEVWire. (2026, October 28). vLLM: Phi-4 multimodal audio encoder materializes and retains full attention matrices: a single max-length audio OOM-crashes the vLLM engine (CVE-2026-115913). https://kevwire.com/record/CVE-2026-115913

MLA

"vLLM: Phi-4 multimodal audio encoder materializes and retains full attention matrices: a single max-length audio OOM-crashes the vLLM engine (CVE-2026-115913)." KEVWire, 28 Oct. 2026, kevwire.com/record/CVE-2026-115913.

Chicago

KEVWire. "vLLM: Phi-4 multimodal audio encoder materializes and retains full attention matrices: a single max-length audio OOM-crashes the vLLM engine (CVE-2026-115913)." Last modified October 28, 2026. https://kevwire.com/record/CVE-2026-115913.

Evidence summary (plain text)

vLLM: Phi-4 multimodal audio encoder materializes and retains full attention matrices: a single max-length audio OOM-crashes the vLLM engine (Oct 28, 2026).
The Phi-4 multimodal audio encoder (phi4mm_audio.py / phi4mm_utils.py) runs its conformer attention in eager mode by default: the official HF config of microsoft/Phi-4-multimodal-instruct does not set use_pt_scaled_dot_product_attention, so the default False applies (phi4mm_audio.py:166, :876; phi4mm_utils.py:1669).
CVSS 6.5 (version 3.1; evidence as of Oct 28, 2026; https://github.com/advisories/GHSA-8397-8qf6-f2gm).
EPSS 0.3% for the following 30 days, scored Oct 28, 2026 (https://api.first.org/data/v1/epss?cve=CVE-2026-115913&date=2026-10-28).
Sources:
GitHub-reviewed advisory GHSA-8397-8qf6-f2gm: https://github.com/advisories/GHSA-8397-8qf6-f2gm
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc
FIRST measured EPSS score: https://api.first.org/data/v1/epss?cve=CVE-2026-115913&date=2026-10-28
Record ID: CVE-2026-115913.
Record origin: Ingested record.
AI evidence: Not assessed.
Known exploitation: Not assessed.
Zero-day status: Not assessed. KEV membership alone does not establish zero-day status.
About this data

This record was ingested from a data source. Retrieved: Oct 29, 2026, 02:16 UTC. Ingestion is not an independent editorial investigation.

A CISA KEV listing documents known exploitation, not necessarily a zero-day; absence from the catalog does not prove a vulnerability has never been exploited.

Technology tags indicate possible relevance, not verified exposure. Confirm the affected product, version, and configuration against your own inventory; KEVWire does not scan your systems.

A personal reading checklist on this device, not verified remediation.

Record JSON

Your account

Email alerts

Email alerts are coming as part of KEVWire Premium: join the waitlist. To follow your stack today, use its RSS feed, which honors your filters, or connect an AI agent through the API.

Suggest an improvement

Ideas, missing sources, corrections, and bugs are all welcome. No account needed. Suggestions are reviewed by a person before anything changes.

Record details

Record detail