Vulnerabilities in pkg:pypi/vllm
PACKAGECVE-2026-105753 is a MODERATE (CVSS 6.5) mirrored multimodal ipc caches desync vulnerability (CWE-617: Reachable Assertion) affecting vLLM libraries in the PyPI ecosystem. The issue involves 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. Remediation is available by upgrading affected packages to version 0.28.0.
pkg:pypi/vllm
Vulnerability advisories affecting package pkg:pypi/vllm. Select an advisory below to view technical details or fixes.
| Advisory ID | CVE / NVD | Severity | Summary |
|---|---|---|---|
| GHSA-ph3r-5jfg-f84f | CVE-2026-105753 | MODERATE | 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 |
| GHSA-935w-9g4m-p28p | CVE-2026-105752 | LOW | vLLM: Harmony tool continuations drop `cache_salt` — restoring a cross-tenant prefix-cache membership oracle |
| GHSA-x6mc-67gf-chw4 | CVE-2026-105758 | MODERATE | vLLM: Qwen2-VL / Qwen3-VL video samplers bound on request-controlled max_frames, which the num_frames ceiling does not reach |
| GHSA-58v5-2m8f-94pr | CVE-2026-105760 | MODERATE | vLLM: GLMGA video sampling permits request-driven CPU and memory exhaustion |
| GHSA-ph72-cqr5-qpp7 | CVE-2026-105754 | MODERATE | vLLM: Scale-out disaggregated multimodal transport trusts caller-supplied features |
| GHSA-85xf-c7hm-whqw | CVE-2026-105757 | MODERATE | vLLM: Structured-output request errors escape the request boundary and terminate the shared EngineCore — engine-fatal denial of service (3 sites) |
| GHSA-2phq-3phc-84px | CVE-2026-105755 | MODERATE | vLLM: Flash late-interaction scoring caches query embeddings under a caller-controlled request id — cross-request integrity break and induced errors on `/score` and `/rerank` |
| GHSA-2823-qmq8-rwvj | CVE-2026-105756 | MODERATE | vLLM: Loose `cache_salt` validation lets a single request kill EngineCore on LMCache-MP deployments — uncaught downstream `ValueError` denial of service |
| GHSA-8pw2-6jv3-mj5j | CVE-2026-69147 | MODERATE | vLLM: Request-selected PyNvVideoCodec GPU decode bypasses static VRAM reservation |
| GHSA-hcwq-8wjf-3gcr | CVE-2026-57173 | MODERATE | vLLM: Unauthenticated audio decompression-bomb DoS in /v1/chat/completions |
| GHSA-mcmc-2m55-j8jj | CVE-2026-56340 | HIGH | vLLM introduced enhanced protection for CVE-2025-62164 |
| GHSA-78fp-cf4h-g36p | - | HIGH | Duplicate Advisory: vLLM introduced enhanced protection for CVE-2025-62164 |
| GHSA-vfm7-4h43-gp6m | - | MODERATE | Duplicate Advisory: vLLM Vulnerable to Regular Expression Denial of Service |
| GHSA-4hhp-h66f-j5j7 | CVE-2026-73560 | MODERATE | vLLM: SSRF + arbitrary local file read in MiMoV2OmniMultiModalProcessor `_fetch_image` and audio loader bypass MediaConnector protections |
| GHSA-7m6h-x95x-82q5 | CVE-2026-73558 | MODERATE | vLLM: Cross-User Data Leak Vulnerability |
| GHSA-pr7f-p5mw-fc87 | CVE-2026-73557 | MODERATE | vLLM: Incomplete CVE-2025-62164 remediation can be bypassed by concurrent prompt parts |
| GHSA-48jh-3gj7-fg8v | CVE-2026-73556 | MODERATE | vLLM: ReDoS via structured_outputs.regex in the lm-format-enforcer backend (no compile timeout) — missed sibling of GHSA-rwxx-mrjm-wc2m |
| GHSA-hwrm-c4cx-rf4j | CVE-2026-73555 | MODERATE | vLLM: Unauthenticated Internal Path and Username Disclosure via Validation Error Messages |
| GHSA-8737-qx52-hjff | CVE-2026-71486 | MODERATE | vLLM: Derender endpoints decode caller-supplied GenerateResponse token IDs without output bounds |
| GHSA-q8gq-377p-jq3r | CVE-2026-41523 | HIGH | vLLM: Security Check Bypass via assert Statement in Activation Function Loading Allows Arbitrary Code Execution |
| GHSA-94f4-hr76-p5j6 | CVE-2026-48746 | CRITICAL | vLLM: OpenAI auth bypass |
| GHSA-pq5c-rjhq-qp7p | CVE-2026-34755 | MODERATE | vLLM: Denial of Service via Unbounded Frame Count in video/jpeg Base64 Processing |
| GHSA-wcwg-c5fc-9vrc | CVE-2026-5497 | HIGH | vLLM is vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method |
| GHSA-87x5-vmc3-756j | CVE-2026-73559 | MODERATE | vLLM: Completion prompt lists fan out into unbounded engine requests |
| GHSA-qh4c-xf7m-gxfc | CVE-2026-24779 | HIGH | vLLM vulnerable to Server-Side Request Forgery (SSRF) through MediaConnector |
| GHSA-2pc9-4j83-qjmr | CVE-2026-22807 | HIGH | vLLM affected by RCE via auto_map dynamic module loading during model initialization |
| GHSA-33cg-gxv8-3p8g | CVE-2026-55514 | HIGH | vLLM denial of service via prompt embeds on M-RoPE models |
| GHSA-hgg8-fqqc-vfmw | CVE-2026-54236 | MODERATE | vLLM: incomplete CVE-2026-22778 fix leaks PIL repr addresses via Anthropic router |
| GHSA-v82g-2437-67m2 | CVE-2026-55646 | MODERATE | vLLM: Speech-to-text upload size limit is enforced after full UploadFile read |
| GHSA-rwxx-mrjm-wc2m | CVE-2026-55574 | HIGH | vLLM: ReDoS via structured_outputs.regex compiled without timeout in xgrammar and outlines backends |
| GHSA-8wr5-jm2h-8r4f | CVE-2026-54234 | HIGH | vLLM has Remote DoS via Invalid Recovered Token Reinjection |
| GHSA-6c4r-fmh3-7rh8 | CVE-2026-34760 | MODERATE | vLLM: Processing differential in multi-channel audio downmixing enables hidden-input/moderation bypass for audio models |
| GHSA-7h4p-rffg-7823 | CVE-2026-54235 | MODERATE | vLLM: temperature=NaN and temperature=Infinity bypass validation and propagate to GPU kernels |
| GHSA-6pr9-rp53-2pmc | CVE-2026-54233 | MODERATE | vLLM: OOM Denial of Service via Audio Decompression Bomb |
| GHSA-8jr5-v98p-w75m | CVE-2026-12491 | MODERATE | vLLM: image EXIF Rotation & PNG tRNS Transparency Not Normalized, Causing Mismatch Between Model Input and Expectations |
| GHSA-3ww4-5jv9-j5gm | CVE-2026-47155 | MODERATE | vLLM's Artifact Pin Decay allows pinned deployments to load unpinned code, weights, and processors |
| GHSA-hpv8-x276-m59f | CVE-2026-44222 | MODERATE | vLLM Vulnerable to Remote DoS via Special-Token Placeholders |
| GHSA-5jv2-g5wq-cmr4 | CVE-2026-53923 | MODERATE | vLLM: GGUF dequantize kernel int truncation exposes uninitialized GPU memory in multi-tenant serving |
| GHSA-3mwp-wvh9-7528 | CVE-2026-34756 | MODERATE | vLLM: Unauthenticated OOM Denial of Service via Unbounded `n` Parameter in OpenAI API Server |
| GHSA-pf3h-qjgv-vcpr | CVE-2026-34753 | MODERATE | vLLM: Server-Side Request Forgery (SSRF) in `download_bytes_from_url ` |
| GHSA-7972-pg2x-xr59 | CVE-2026-27893 | HIGH | vLLM has Hardcoded Trust Override in Model Files Enables RCE Despite Explicit User Opt-Out |
| GHSA-v359-jj2v-j536 | CVE-2026-25960 | MODERATE | vLLM has SSRF Protection Bypass |
| GHSA-4r2x-xpjr-7cvv | CVE-2026-22778 | CRITICAL | vLLM has RCE In Video Processing |
| GHSA-8fr4-5q9j-m8gm | CVE-2025-66448 | HIGH | vLLM vulnerable to remote code execution via transformers_utils/get_config |
| GHSA-69j4-grxj-j64p | CVE-2025-62426 | MODERATE | vLLM vulnerable to DoS via large Chat Completion or Tokenization requests with specially crafted `chat_template_kwargs` |
| GHSA-pmqf-x6x8-p7qw | CVE-2025-62372 | HIGH | vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs |
| GHSA-mrw7-hf4f-83pf | CVE-2025-62164 | HIGH | vLLM deserialization vulnerability leading to DoS and potential RCE |
| GHSA-wr9h-g72x-mwhm | CVE-2025-59425 | HIGH | vLLM is vulnerable to timing attack at bearer auth |
| GHSA-rxc4-3w6r-4v47 | CVE-2025-48956 | HIGH | vllm API endpoints vulnerable to Denial of Service Attacks |
| GHSA-vrq3-r879-7m65 | CVE-2025-48944 | MODERATE | vLLM Tool Schema allows DoS via Malformed pattern and type Fields |
| GHSA-hjq4-87xh-g4fv | CVE-2025-47277 | CRITICAL | vLLM Allows Remote Code Execution via PyNcclPipe Communication Service |
| GHSA-vc6m-hm49-g9qg | CVE-2025-46560 | MODERATE | vLLM: Quadratic Time Complexity in Input Token Processing leads to denial of service |
| GHSA-9f8f-2vmf-885j | CVE-2025-30202 | HIGH | Data exposure via ZeroMQ on multi-node vLLM deployment |
| GHSA-9pcc-gvx5-r5wm | CVE-2025-30165 | HIGH | Remote Code Execution Vulnerability in vLLM Multi-Node Cluster Configuration |
| GHSA-98f3-hwg4-4rf7 | CVE-2026-9540 | MODERATE | vllm has Improper Resource Shutdown or Release |
| GHSA-x8xr-mj9x-6h7w | - | MODERATE | Duplicate Advisory: image EXIF Rotation & PNG tRNS Transparency Not Normalized, Causing Mismatch Between Model Input and Expectations |
| GHSA-83vm-p52w-f9pw | CVE-2026-44223 | MODERATE | vLLM: extract_hidden_states speculative decoding crashes server on any request with penalty parameters |
| GHSA-grg2-63fw-f2qr | CVE-2026-22773 | MODERATE | vLLM is vulnerable to DoS in Idefics3 vision models via image payload with ambiguous dimensions |
| GHSA-cj47-qj6g-x7r4 | CVE-2024-9053 | CRITICAL | vLLM allows Remote Code Execution by Pickle Deserialization via AsyncEngineRPCServer() RPC server entrypoints |
| GHSA-mgrm-fgjv-mhv8 | CVE-2025-29770 | MODERATE | vLLM denial of service via outlines unbounded cache on disk |
| GHSA-x368-4g9h-fvv4 | CVE-2026-7141 | LOW | vLLM makes Use of Uninitialized Resource |
| GHSA-3f6c-7fw2-ppm4 | CVE-2025-6242 | HIGH | vLLM is vulnerable to Server-Side Request Forgery (SSRF) through `MediaConnector` class |
| GHSA-6fvq-23cw-5628 | CVE-2025-61620 | MODERATE | vLLM: Resource-Exhaustion (DoS) through Malicious Jinja Template in OpenAI-Compatible Server |
| GHSA-79j6-g2m3-jgfw | CVE-2025-9141 | HIGH | vLLM has remote code execution vulnerability in the tool call parser for Qwen3-Coder |
| GHSA-x3m8-f7g5-qhm7 | CVE-2025-29783 | CRITICAL | vLLM Allows Remote Code Execution via Mooncake Integration |
| GHSA-rm76-4mrf-v9r8 | CVE-2025-25183 | LOW | vLLM uses Python 3.12 built-in hash() which leads to predictable hash collisions in prefix cache |
| GHSA-rh4j-5rhw-hr54 | CVE-2025-24357 | HIGH | vllm: Malicious model to RCE by torch.load in hf_model_weights_iterator |
| GHSA-9hcf-v7m4-6m2j | CVE-2025-48943 | MODERATE | vLLM allows clients to crash the openai server with invalid regex |
| GHSA-6qc9-v4r8-22xg | CVE-2025-48942 | MODERATE | vLLM DOS: Remotely kill vllm over http with invalid JSON schema |
| GHSA-4qjh-9fv9-r85r | CVE-2025-46570 | LOW | Potential Timing Side-Channel Vulnerability in vLLM’s Chunk-Based Prefix Caching |
| GHSA-w6q7-j642-7c25 | CVE-2025-48887 | MODERATE | vLLM has a Regular Expression Denial of Service (ReDoS, Exponential Complexity) Vulnerability in `pythonic_tool_parser.py` |
| GHSA-c65p-x677-fgj6 | CVE-2025-46722 | MODERATE | vLLM has a Weakness in MultiModalHasher Image Hashing Implementation |
| GHSA-hj4w-hm2g-p6w5 | CVE-2025-32444 | CRITICAL | vLLM Vulnerable to Remote Code Execution via Mooncake Integration |
| GHSA-j828-28rj-hfhp | - | MODERATE | vLLM vulnerable to Regular Expression Denial of Service |
| GHSA-ggpf-24jw-3fcw | - | CRITICAL | CVE-2025-24357 Malicious model remote code execution fix bypass with PyTorch < 2.6.0 |
| GHSA-hf3c-wxg2-49q9 | - | MODERATE | vLLM vulnerable to Denial of Service by abusing xgrammar cache |
| GHSA-pgr7-mhp5-fgjp | CVE-2024-9052 | CRITICAL | vLLM deserialization vulnerability in vllm.distributed.GroupCoordinator.recv_object |
| GHSA-5vqr-wprc-cpp7 | CVE-2024-11041 | CRITICAL | vLLM Deserialization of Untrusted Data vulnerability |
| GHSA-wc36-9694-f9rf | CVE-2024-8939 | MODERATE | vLLM Denial of Service via the best_of parameter |
| GHSA-w2r7-9579-27hf | CVE-2024-8768 | HIGH | vLLM denial of service vulnerability |
CVE-2026-105753 at a Glance
Security Impact
CVSS: 6.5
The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H).
How to Fix CVE-2026-105753
To remediate this vulnerability, upgrade all affected vLLM dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.
pip install vllm>=0.28.0
Run the following command in your project directory to verify that all resolved dependencies use the patched release:
pip show vllm
How to Check if You Are Affected
To determine whether your Python environment is affected by CVE-2026-105753, inspect your installed packages using pip:
pip show vllm
Analyze the terminal output to locate any version of vLLM in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 0.28.0), your application is affected.
Note: The pip show vllm command checks currently installed packages in your active Python environment.
Technical Details
Vulnerability Mechanism & Technical Analysis
This vulnerability is classified under CWE-617: Reachable Assertion, where the product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary. In vLLM, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.
Source Advisory Statement
CVE-2026-105753 Affected vLLM Versions
1 component(s)| Product / Package | Ecosystem | Affected Versions | Fixed Version |
|---|---|---|---|
|
vllm
|
PyPI |
< 0.28.0
|
0.28.0
|
References
Frequently Asked Questions
What is CVE-2026-105753?
Which vLLM versions are affected?
What version fixes CVE-2026-105753?
Is vllm affected?
What is the CVSS score of CVE-2026-105753?
What CWE is associated with CVE-2026-105753?
How can I check PyPI dependencies for CVE-2026-105753?
Machine-readable OSV JSON
OSV v1.5 Schema
{
"schema_version" : "1.4.0",
"id" : "GHSA-ph3r-5jfg-f84f",
"modified" : "2026-10-06T00:02:06Z",
"published" : "2026-10-06T00:02:05Z",
"aliases" : [ "CVE-2026-105753" ],
"summary" : "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",
"details" : "## Affected\n\n- **Ecosystem / package:** pip / `vllm`\n- **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.\n\n## Summary\n\nvLLM'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.\n\nThat 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=}\"`.\n\nThis is a remotely reachable, request-controlled cache-mirroring desync on the standard multimodal inference path. It requires only the default cache configuration.\n\n## Affected code\n\nLinks pinned to the confirmed commit [`752a3a504485`](https://github.com/vllm-project/vllm/tree/752a3a504485790a2e8491cacbb35c137339ad34) (v0.25.1):\n\n- **P0 metadata cache** — `MultiModalProcessorSenderCache` at [`vllm/multimodal/cache.py#L379`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/multimodal/cache.py#L379); `get_and_update_item` at [`#L410-L421`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/multimodal/cache.py#L410-L421), commit assertion at [`#L418`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/multimodal/cache.py#L418).\n- **P1 payload cache (the actual sink)** — `MultiModalReceiverCache` at [`vllm/multimodal/cache.py#L630`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/multimodal/cache.py#L630); `get_and_update_item` at [`#L652-L663`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/multimodal/cache.py#L652-L663), with the failing `assert mm_item is not None, f\"Expected a cached item for {mm_hash=}\"` at [`#L660`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/multimodal/cache.py#L660).\n- **Default `mm_processor_cache_type = \"lru\"`** at [`vllm/config/multimodal.py#L132`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/config/multimodal.py#L132); dispatch in [`vllm/multimodal/registry.py#L294-L307`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/multimodal/registry.py#L294-L307) (sender) and [`#L322-L331`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/multimodal/registry.py#L322-L331) (receiver). The `processor_only`, disabled-caching, and `shm` paths are not affected.\n- **Rejection-after-render window** — rendering happens before length validation in [`vllm/entrypoints/openai/chat_completion/serving.py#L206-L231`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/entrypoints/openai/chat_completion/serving.py#L206-L231) (`render_chat_request`), and the length check raises after the render in [`vllm/entrypoints/serve/utils/api_utils.py#L171-L189`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/entrypoints/serve/utils/api_utils.py#L171-L189).\n- **Cache-commit call site** — [`vllm/multimodal/processing/processor.py#L1347`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/multimodal/processing/processor.py#L1347) (`_merge_mm_kwargs`) commits the P0 sender entry during render.\n- The separate stale-order eviction hang is already fixed via [`vllm/utils/cache.py#L120-L121`](https://github.com/vllm-project/vllm/blob/752a3a504485790a2e8491cacbb35c137339ad34/vllm/utils/cache.py#L120-L121) (`LRUCache.touch()` guarding `if key in self:`) — a different mechanism that does not touch the sender/receiver commit-ordering protocol and does not remediate this assertion.\n\nThe P1 receiver sink — the assertion that fires when P1 receives `None` for a hash it never cached:\n\n```python\n# vllm/multimodal/cache.py Lines 651-663\n @override\n def get_and_update_item(\n self,\n mm_item: MultiModalKwargsItem | None,\n mm_hash: str,\n ) -> MultiModalKwargsItem:\n if (cached_item := self._cache.get(mm_hash)) is not None:\n return cached_item\n\n assert mm_item is not None, f\"Expected a cached item for {mm_hash=}\"\n\n self._cache[mm_hash] = mm_item\n return mm_item\n```\n\nThe 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:\n\n```python\n# vllm/multimodal/cache.py Lines 409-422\n @override\n def get_and_update_item(\n self,\n mm_item: MultiModalProcessorCacheInItem,\n mm_hash: str,\n ) -> MultiModalProcessorCacheOutItem:\n if (cached_item := self._cache.get(mm_hash)) is not None:\n return None, cached_item.prompt_updates\n\n assert mm_item is not None, f\"Expected a cached item for {mm_hash=}\"\n\n self._cache[mm_hash] = MultiModalProcessorCacheItemMetadata(*mm_item)\n\n return mm_item\n```\n\n## Impact\n\nA 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.\n\nOn 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.\n\n\n## Suggested Fix\n\nTwo complementary changes:\n\n1. **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.\n2. **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.\n\nThe 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`:\n\n```python\n# vllm/entrypoints/openai/chat_completion/serving.py (_create_chat_completion)\n- max_tokens = get_max_tokens(\n- max_model_len,\n- ...,\n- truncate_prompt_tokens=request.truncate_prompt_tokens,\n- )\n+ try:\n+ max_tokens = get_max_tokens(\n+ max_model_len,\n+ ...,\n+ truncate_prompt_tokens=request.truncate_prompt_tokens,\n+ )\n+ except ValueError:\n+ for rendered_input in engine_inputs:\n+ if mm_hashes := rendered_input.get(\"mm_hashes\"):\n+ self.renderer.discard_mm_cache_entries(mm_hashes)\n+ raise\n```\n\n```python\n# vllm/multimodal/cache.py (MultiModalProcessorSenderCache)\n+ @override\n+ def discard_sender_cache_item(self, mm_hash: str) -> None:\n+ self._cache.pop(mm_hash, None)\n```\n\nThis 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.\n\n## Credit\n\n**Reported by:** Patch the Planet (Trail of Bits + OpenAI collaboration)\n\nThis vulnerability was discovered using GPT-5.5-Cyber as part of the Patch the Planet security initiative.\n\n---\n\n**Proposed fix:** a fix for this issue is proposed in a public pull request: https://github.com/vllm-project/vllm/pull/51897",
"severity" : [ {
"type" : "CVSS_V3",
"score" : "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H"
} ],
"affected" : [ {
"package" : {
"ecosystem" : "PyPI",
"name" : "vllm"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "0.28.0"
} ]
} ]
} ],
"references" : [ {
"type" : "WEB",
"url" : "https://github.com/vllm-project/vllm/security/advisories/GHSA-ph3r-5jfg-f84f"
}, {
"type" : "WEB",
"url" : "https://github.com/vllm-project/vllm/pull/46747"
}, {
"type" : "WEB",
"url" : "https://github.com/vllm-project/vllm/pull/51897"
}, {
"type" : "WEB",
"url" : "https://github.com/vllm-project/vllm/commit/396204230423b7cc6798300926b8fa30190d26a9"
}, {
"type" : "PACKAGE",
"url" : "https://github.com/vllm-project/vllm"
}, {
"type" : "WEB",
"url" : "https://github.com/vllm-project/vllm/releases/tag/v0.28.0"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-617" ],
"severity" : "MODERATE",
"github_reviewed" : true,
"github_reviewed_at" : "2026-10-06T00:02:05Z",
"nvd_published_at" : null
}
}