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MEDIUM severity

CVE-2026-105753 — vllm

MEDIUMFix: vllm-project/vllm@3962042

CVE-2026-105753 is a medium-severity (CVSS 6.5) Reachable Assertion vulnerability in vllm. A fix is available for vllm — see the affected versions and patch details below.

vLLM: Mirrored multimodal IPC caches desync after a rejected request — a later request reusing the same media hash trips a receiver assertion in the engine core

Also known asGHSA-ph3r-5jfg-f84fPYSEC-2026-4197
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Oct 22, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

  • CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.

Exploitation and automatability from CISA’s SSVC triage for CVE-2026-105753.

EPSS Exploitation Probability

via FIRST.org ↗
0.4%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs36th percentile — riskier than 36% of all scored CVEsHighest risk

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

CVE-2026-105753 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.

Where this sits among everything scored

Of 394,803 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.

Real-World Exposure

1 pkg affected
🐍vllm

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Affected

  • Ecosystem / package: pip / vllm
  • Affected versions: vLLM ≤ 0.25.1 (confirmed on 0.25.1, commit 752a3a504485). The lower bound predates 0.25.1; maintainers can confirm how far back the mirrored sender/receiver cache protocol reaches.

Summary

vLLM's default multimodal cache (mm_processor_cache_type="lru") mirrors state across two processes: the frontend (P0) holds only metadata (MultiModalProcessorSenderCache) while the engine core (P1) holds the real payload (MultiModalReceiverCache). The design invariant is that get_and_update() runs on P0 and P1 in lockstep for every request, so eviction order stays mirrored and P0 can answer "is this cached in P1?" without talking to P1.

That invariant breaks when a request is rejected after P0 has rendered and hashed the multimodal input (populating the P0 cache) but before P1 receives the item — for example, an oversized chat prompt rejected on max_model_len after rendering. P0 now believes the media is cached while P1 never got it. A later request reusing the same media hash gets a P0 hit, so P0 sends None instead of the payload, and P1 — which has nothing cached — trips assert mm_item is not None, f"Expected a cached item for {mm_hash=}".

This is a remotely reachable, request-controlled cache-mirroring desync on the standard multimodal inference path. It requires only the default cache configuration.

Affected code

Links pinned to the confirmed commit 752a3a504485 (v0.25.1):

The P1 receiver sink — the assertion that fires when P1 receives None for a hash it never cached:

# vllm/multimodal/cache.py Lines 651-663
    @override
    def get_and_update_item(
        self,
        mm_item: MultiModalKwargsItem | None,
        mm_hash: str,
    ) -> MultiModalKwargsItem:
        if (cached_item := self._cache.get(mm_hash)) is not None:
            return cached_item

        assert mm_item is not None, f"Expected a cached item for {mm_hash=}"

        self._cache[mm_hash] = mm_item
        return mm_item

The P0 sender — on a hit it drops the payload (returns None) and, on a miss during render, unconditionally commits the metadata entry with no rollback tied to admission:

# vllm/multimodal/cache.py Lines 409-422
    @override
    def get_and_update_item(
        self,
        mm_item: MultiModalProcessorCacheInItem,
        mm_hash: str,
    ) -> MultiModalProcessorCacheOutItem:
        if (cached_item := self._cache.get(mm_hash)) is not None:
            return None, cached_item.prompt_updates

        assert mm_item is not None, f"Expected a cached item for {mm_hash=}"

        self._cache[mm_hash] = MultiModalProcessorCacheItemMetadata(*mm_item)

        return mm_item

Impact

A remote client submitting multimodal requests can poison a cache identity — render a media item successfully, then have that request rejected — so a later request reusing the same media hash fails on the P1 receiver assertion. This is an availability failure against a shared serving instance. No code execution, memory corruption, or data disclosure is claimed.

On this revision the failure is scoped as a request-level preprocessing error (the engine core catches around preprocess_add_request); public reports show the same assertion cascading into further engine-loop assertions on other revisions. It applies to multimodal models running the default mirrored lru cache.

Suggested Fix

Two complementary changes:

  1. Make the mirrored commit atomic with admission — insert into the P0 sender cache only after the request has passed all admission checks (length, limits) and P1 has acknowledged the item, or roll back the P0 insert on rejection.
  2. Defense in depth — convert the P1 receiver assert mm_item is not None into a checked, request-scoped error (fetch-on-miss from P0) so a desync degrades a single request rather than asserting in the engine loop.

The core of the rollback half: wrap the post-render length check so a ValueError rejection discards the P0 entries the render just committed, before re-raising. Add a discard_sender_cache_item() on the processor cache (no-op default, pop on the sender) and a Renderer.discard_mm_cache_entries() that walks a rendered request's mm_hashes:

# vllm/entrypoints/openai/chat_completion/serving.py (_create_chat_completion)
-            max_tokens = get_max_tokens(
-                max_model_len,
-                ...,
-                truncate_prompt_tokens=request.truncate_prompt_tokens,
-            )
+            try:
+                max_tokens = get_max_tokens(
+                    max_model_len,
+                    ...,
+                    truncate_prompt_tokens=request.truncate_prompt_tokens,
+                )
+            except ValueError:
+                for rendered_input in engine_inputs:
+                    if mm_hashes := rendered_input.get("mm_hashes"):
+                        self.renderer.discard_mm_cache_entries(mm_hashes)
+                raise
# vllm/multimodal/cache.py (MultiModalProcessorSenderCache)
+    @override
+    def discard_sender_cache_item(self, mm_hash: str) -> None:
+        self._cache.pop(mm_hash, None)

This closes the max_model_len rejection path; because any other rejection-after-render path reopens the same window, pairing it with the defense-in-depth change above (making the P1 assert a checked, request-scoped error) is recommended.

Credit

Reported by: Patch the Planet (Trail of Bits + OpenAI collaboration)

This vulnerability was discovered using GPT-5.5-Cyber as part of the Patch the Planet security initiative.


Proposed fix: a fix for this issue is proposed in a public pull request: https://github.com/vllm-project/vllm/pull/51897

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIvllmall versions0.28.0pip install --upgrade 'vllm==0.28.0'

Affected Products

1 product · 1 configurations
Application
vllmvllm
< 0.28.0
range

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for vllm, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update vllm to 0.28.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-105753 is resolved across your whole dependency graph.

  3. Workarounds

    Stop feeding it untrusted input: reject or quarantine files and payloads from unverified sources until you can upgrade, restrict accepted formats to the ones you actually need, and run the parsing or decoding step in a least-privileged sandbox or short-lived worker so a crash or corrupted read cannot reach the rest of the process.

Fixing This On Your OS

If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.

Red HatModerate

A MODERATE severity denial of service vulnerability was found in vLLM as shipped in Red Hat AI Inference Server 3, Red Hat OpenShift AI, and Red Hat Enterprise Linux AI. When a multimodal request is rejected after its media has been rendered and hashed (for example, a chat prompt that exceeds the model's maximum…

Workaround published by Red Hat
The vulnerability affects only vLLM's default mirrored LRU multimodal processor cache configuration; the upstream advisory states that the shared-memory FIFO cache path and fully disabled caching are not affected. Administrators can work around this issue by changing the multimodal processor cache configuration: either set mm_processor_cache_type to "shm" to use the shared-memory FIFO cache instead of the mirrored LRU cache, or set mm_processor_cache_gb to 0 to disable the multimodal processor cache entirely (upstream notes that fully disabling this cache is not recommended because past multimodal inputs must then be re-processed). Restricting access to the inference API to trusted clients…
Source: Red Hat security advisory for CVE-2026-105753 (CC BY 4.0)

Frequently Asked Questions

## Affected - **Ecosystem / package:** pip / `vllm` - **Affected versions:** vLLM ≤ 0.25.1 (confirmed on 0.25.1, commit [`752a3a504485`](https://github.com/vllm-project/vllm/tree/752a3a504485790a2e8491cacbb35c137339ad34)). The lower bound predates 0.25.1; maintainers can confirm how far back the mirrored sender/receiver cache protocol reaches. ## Summary vLLM's default multimodal cache (`mm_processor_cache_type="lru"`) mirrors state across two processes: the frontend (P0) holds only metadata (`MultiModalProcessorSenderCache`) while the engine core (P1) holds the real payload (`MultiModalRec
O3 Security · Impact-Aware SCA

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CVE-2026-105753: vllm — Fixed in 0.28.0