<?xml version='1.0' encoding='UTF-8'?>
<?xml-stylesheet href="/static/style.xsl" type="text/xsl"?>
<rss xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/" version="2.0">
  <channel>
    <title>Most recent entries from all</title>
    <link>https://cve.radiocsirt.org</link>
    <description>Contains only the most 10 recent entries.</description>
    <docs>http://www.rssboard.org/rss-specification</docs>
    <generator>python-feedgen</generator>
    <language>en</language>
    <lastBuildDate>Sun, 04 Oct 2026 17:08:52 +0000</lastBuildDate>
    <item>
      <title>EUVD-2026-317959</title>
      <link>https://cve.radiocsirt.org/vuln/euvd-2026-317959</link>
      <description>EUVD-2026-317959</description>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/euvd-2026-317959</guid>
    </item>
    <item>
      <title>fkie_cve-2026-44222</title>
      <link>https://cve.radiocsirt.org/vuln/fkie_cve-2026-44222</link>
      <description>&lt;p&gt;vLLM is an inference and serving engine for large language models (LLMs). From 0.6.1 to before 0.20.0, there is a a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on image_grid_thw/video_grid_thw are affected. This vulnerability is fixed in 0.20.0.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;vLLM is an inference and serving engine for large language models (LLMs). From 0.6.1 to before 0.20.0, there is a a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on image_grid_thw/video_grid_thw are affected. This vulnerability is fixed in 0.20.0.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/fkie_cve-2026-44222</guid>
    </item>
    <item>
      <title>GHSA-hpv8-x276-m59f — vLLM Vulnerable to Remote DoS via Special-Token Placeholders</title>
      <link>https://cve.radiocsirt.org/vuln/ghsa-hpv8-x276-m59f</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: vllm&lt;/p&gt;
&lt;p&gt;## Summary
This report explains a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on `image_grid_thw`/`video_grid_thw` are affected. Severity: High (remote DoS). Reproduced on vLLM 0.10.0 with Qwen2.5-VL.&lt;/p&gt;
&lt;p&gt;## Details
- Affected component: multimodal input position computation.
- File/functions (paths are indicative):
  - vllm/model_executor/layers/rotary_embedding.py
    - get_input_positions_tensor(...)
    - _vl_get_input_positions_tensor(...)
- Failure mechanism:
  - The code counts detected vision tokens and then indexes video_grid_thw/image_grid_thw accordingly.
  - When user input carries placeholder tokens but no actual multimodal payload, these grids are empty. The code does not bounds-check before indexing.&lt;/p&gt;
&lt;p&gt;Representative snippet (context):
```python
# vllm/model_executor/layers/rotary_embedding.py
@classmethod
def _vl_get_input_positions_tensor(
    cls,
    input_tokens,
    hf_config,
    image_grid_thw,
    video_grid_thw,
    ...,
):
    # detect video tokens
    video_nums = (vision_tokens == video_token_id).sum()
    # later in processing
    t, h, w = (
        video_grid_thw[video_index][0],  # IndexError…&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: vllm&lt;/p&gt;
&lt;p&gt;## Summary
This report explains a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on `image_grid_thw`/`video_grid_thw` are affected. Severity: High (remote DoS). Reproduced on vLLM 0.10.0 with Qwen2.5-VL.&lt;/p&gt;
&lt;p&gt;## Details
- Affected component: multimodal input position computation.
- File/functions (paths are indicative):
  - vllm/model_executor/layers/rotary_embedding.py
    - get_input_positions_tensor(...)
    - _vl_get_input_positions_tensor(...)
- Failure mechanism:
  - The code counts detected vision tokens and then indexes video_grid_thw/image_grid_thw accordingly.
  - When user input carries placeholder tokens but no actual multimodal payload, these grids are empty. The code does not bounds-check before indexing.&lt;/p&gt;
&lt;p&gt;Representative snippet (context):
```python
# vllm/model_executor/layers/rotary_embedding.py
@classmethod
def _vl_get_input_positions_tensor(
    cls,
    input_tokens,
    hf_config,
    image_grid_thw,
    video_grid_thw,
    ...,
):
    # detect video tokens
    video_nums = (vision_tokens == video_token_id).sum()
    # later in processing
    t, h, w = (
        video_grid_thw[video_index][0],  # IndexError…&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/ghsa-hpv8-x276-m59f</guid>
    </item>
    <item>
      <title>PYSEC-2026-3409 — vLLM Vulnerable to Remote DoS via Special-Token Placeholders</title>
      <link>https://cve.radiocsirt.org/vuln/pysec-2026-3409</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: vllm&lt;/p&gt;
&lt;p&gt;## Summary
This report explains a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on `image_grid_thw`/`video_grid_thw` are affected. Severity: High (remote DoS). Reproduced on vLLM 0.10.0 with Qwen2.5-VL.&lt;/p&gt;
&lt;p&gt;## Details
- Affected component: multimodal input position computation.
- File/functions (paths are indicative):
  - vllm/model_executor/layers/rotary_embedding.py
    - get_input_positions_tensor(...)
    - _vl_get_input_positions_tensor(...)
- Failure mechanism:
  - The code counts detected vision tokens and then indexes video_grid_thw/image_grid_thw accordingly.
  - When user input carries placeholder tokens but no actual multimodal payload, these grids are empty. The code does not bounds-check before indexing.&lt;/p&gt;
&lt;p&gt;Representative snippet (context):
```python
# vllm/model_executor/layers/rotary_embedding.py
@classmethod
def _vl_get_input_positions_tensor(
    cls,
    input_tokens,
    hf_config,
    image_grid_thw,
    video_grid_thw,
    ...,
):
    # detect video tokens
    video_nums = (vision_tokens == video_token_id).sum()
    # later in processing
    t, h, w = (
        video_grid_thw[video_index][0],  # IndexError…&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: vllm&lt;/p&gt;
&lt;p&gt;## Summary
This report explains a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on `image_grid_thw`/`video_grid_thw` are affected. Severity: High (remote DoS). Reproduced on vLLM 0.10.0 with Qwen2.5-VL.&lt;/p&gt;
&lt;p&gt;## Details
- Affected component: multimodal input position computation.
- File/functions (paths are indicative):
  - vllm/model_executor/layers/rotary_embedding.py
    - get_input_positions_tensor(...)
    - _vl_get_input_positions_tensor(...)
- Failure mechanism:
  - The code counts detected vision tokens and then indexes video_grid_thw/image_grid_thw accordingly.
  - When user input carries placeholder tokens but no actual multimodal payload, these grids are empty. The code does not bounds-check before indexing.&lt;/p&gt;
&lt;p&gt;Representative snippet (context):
```python
# vllm/model_executor/layers/rotary_embedding.py
@classmethod
def _vl_get_input_positions_tensor(
    cls,
    input_tokens,
    hf_config,
    image_grid_thw,
    video_grid_thw,
    ...,
):
    # detect video tokens
    video_nums = (vision_tokens == video_token_id).sum()
    # later in processing
    t, h, w = (
        video_grid_thw[video_index][0],  # IndexError…&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/pysec-2026-3409</guid>
    </item>
    <item>
      <title>RHSA-2026:60363 — Red Hat Security Advisory: Red Hat AI Inference Server 3.3.6 (Spyre)</title>
      <link>https://cve.radiocsirt.org/vuln/rhsa-2026:60363</link>
      <description>&lt;p&gt;vllm: vLLM: Denial of Service via malformed multimodal input or token injection vllm: vLLM: Supply-chain integrity issue due to inconsistent revision pinning controls vllm: vLLM: Information disclosure via integer truncation vllm: vLLM: Denial of Service via malformed speculative decoding workload vllm: vLLM: Denial of Service via adversarial regular expression in structured outputs API&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;vllm: vLLM: Denial of Service via malformed multimodal input or token injection vllm: vLLM: Supply-chain integrity issue due to inconsistent revision pinning controls vllm: vLLM: Information disclosure via integer truncation vllm: vLLM: Denial of Service via malformed speculative decoding workload vllm: vLLM: Denial of Service via adversarial regular expression in structured outputs API&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/rhsa-2026:60363</guid>
    </item>
    <item>
      <title>WID-SEC-W-2026-1364 — vllm: Schwachstelle ermöglicht Denial of Service</title>
      <link>https://cve.radiocsirt.org/vuln/wid-sec-w-2026-1364</link>
      <description>&lt;p&gt;Ein entfernter, authentisierter Angreifer kann eine Schwachstelle in vllm ausnutzen, um einen Denial of Service Angriff durchzuführen.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;Ein entfernter, authentisierter Angreifer kann eine Schwachstelle in vllm ausnutzen, um einen Denial of Service Angriff durchzuführen.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/wid-sec-w-2026-1364</guid>
    </item>
  </channel>
</rss>
