Vulnerability-Lookup

GHSA-3242-RQGF-8X5V

Vulnerability from github – Published: 2026-02-02 03:31 – Updated: 2026-02-02 03:31

Details

A flaw has been found in Open5GS up to 2.7.6. The impacted element is the function sgwc_tunnel_add of the file /src/sgwc/context.c of the component SGWC. Executing a manipulation of the argument pdr can lead to reachable assertion. The attack can be executed remotely. The exploit has been published and may be used. It is advisable to implement a patch to correct this issue. The issue report is flagged as already-fixed.

Severity

5.3 (Medium)


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

5.5 (Medium)


                  
                    CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:P

Show details on source website

JSON

To clipboard

{
  "affected": [],
  "aliases": [
    "CVE-2026-1738"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-617"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-02-02T02:16:10Z",
    "severity": "MODERATE"
  },
  "details": "A flaw has been found in Open5GS up to 2.7.6. The impacted element is the function sgwc_tunnel_add of the file /src/sgwc/context.c of the component SGWC. Executing a manipulation of the argument pdr can lead to reachable assertion. The attack can be executed remotely. The exploit has been published and may be used. It is advisable to implement a patch to correct this issue. The issue report is flagged as already-fixed.",
  "id": "GHSA-3242-rqgf-8x5v",
  "modified": "2026-02-02T03:31:18Z",
  "published": "2026-02-02T03:31:18Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-1738"
    },
    {
      "type": "WEB",
      "url": "https://github.com/open5gs/open5gs/issues/4261"
    },
    {
      "type": "WEB",
      "url": "https://github.com/open5gs/open5gs/issues/4261#event-21968563677"
    },
    {
      "type": "WEB",
      "url": "https://github.com/open5gs/open5gs/issues/4261#issue-3787803578"
    },
    {
      "type": "WEB",
      "url": "https://github.com/open5gs/open5gs"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?ctiid.343637"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?id.343637"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?submit.741193"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
      "type": "CVSS_V4"
    }
  ]
}

GHSA-33CG-GXV8-3P8G

Vulnerability from github – Published: 2026-07-20 19:13 – Updated: 2026-07-20 19:13

Summary

vLLM denial of service via prompt embeds on M-RoPE models

Details

Summary

Short summary of the problem. Make the impact and severity as clear as possible. For example: An unsafe deserialization vulnerability allows any unauthenticated user to execute arbitrary code on the server.

Sending a pure prompt embeds payload in a /v1/completions request with a model using M-RoPE causes the EngineCore to fail an assertion and fatally crash, shutting down the entire server application.

Any remote user who is authorized to make a /v1/completions endpoint can trivially make such a request and induce a crash.

Details

Give all details on the vulnerability. Pointing to the incriminated source code is very helpful for the maintainer.

In commit 56669c1, a simple assert intended to be a type-narrowing assert was added to the _init_mrope_positions method in GPUModelRunner (the offending line on main at the time of writing: https://github.com/vllm-project/vllm/blob/2d481f8a946ee0521872af0f098674a8ee01ce4a/vllm/v1/worker/gpu_model_runner.py#L1588-L1607).

assert req_state.prompt_token_ids is not None, (
            "M-RoPE requires prompt_token_ids to be available."
        )

This type narrowing assert is to prevent mypy errors later in the function because None is not a valid type for mrope_model.get_mrope_input_positions. Unfortunately, this assertion is not always true. /v1/completions requests that specify prompt=None and prompt_embeds=<not none> will indeed create a CachedRequestState where prompt_token_ids is None. This triggers the assertion, which in turn crashes the EngineCore and the Server application.

(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]   File "/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 3997, in execute_model
(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]     deferred_state_corrections_fn = self._update_states(scheduler_output)
(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]   File "/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 1239, in _update_states
(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]     self._init_mrope_positions(req_state)
(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]   File "/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 1582, in _init_mrope_positions
(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]     assert req_state.prompt_token_ids is not None, (
(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167] AssertionError: M-RoPE requires prompt_token_ids to be available.
(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] AsyncLLM output_handler failed.
(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] Traceback (most recent call last):
(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704]   File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/async_llm.py", line 660, in output_handler
(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704]     outputs = await engine_core.get_output_async()
(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704]               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704]   File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py", line 1030, in get_output_async
(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704]     raise self._format_exception(outputs) from None
(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause.

All requests using the /v1/chat/completions endpoints will have text/prompt_token_ids parts (corresponding to the chat template), and prompt_embeds parts are handled as mm_features. This method (rightly) filters out those prompt_embeds content parts as they are treated as text positions.

A sufficient solution to type narrowing here without raising a fatal assertion is to instead replace the assertion with a using dummy token ids:

def _init_mrope_positions(self, req_state: CachedRequestState):
    model = self.get_model()
    assert supports_mrope(model), "M-RoPE support is not implemented."
    mrope_model = cast(SupportsMRoPE, model)

    # Filter out prompt_embeds modality (text-only position info)
    mrope_features = [
        f for f in req_state.mm_features if f.modality != "prompt_embeds"
    ]

    # Handle both token_ids and embeddings-only inputs
    if req_state.prompt_token_ids is not None:
        input_tokens = req_state.prompt_token_ids
    elif req_state.prompt_embeds is not None:
        # For text-only embeddings, dummy token IDs are safe since
        # get_mrope_input_positions only uses len(input_tokens) when mm_features is empty
        seq_len = req_state.prompt_embeds.shape[0]
        input_tokens = list(range(seq_len))
        # Verify no mm_features remain (should be true after prompt_embeds filter)
        assert len(mrope_features) == 0, (
            "M-RoPE with prompt_embeds-only input should have no multimodal features"
        )
    else:
        raise ValueError(
            "M-RoPE requires either prompt_token_ids or prompt_embeds."
        )

    req_state.mrope_positions, req_state.mrope_position_delta = (
        mrope_model.get_mrope_input_positions(
            input_tokens,
            mrope_features,
        )
    )

Technically, in isolation, this method still crashes in the case where req_state.prompt_token_ids is None and req_state.mm_features, so the solution above still leaves that potential vector open. As far as can be determined, however, such a req_state is impossible in the first place in online mode, because it would require a /v1/completions request with prompt_embeds AND multimodal features, but the /v1/completions request schema does not expose multimodal inputs in any discernible way. Today, those are the only two endpoints with prompt_embeds support.

When in offline mode, it is technically possible to directly create an EngineCoreRequest that has prompt_embeds and not prompt_token_ids and mm_features, and pass that to LLM.generate. That would trigger this same assertion, and no validation would prevent that combination. It is strongly suspected, though, that this combination would be undefined in any model that support M-RoPE, because it would not be possible to determine which token positions correspond to mm_features. The proposed solution above would end up not setting req_state.mrope_positions and req_state.mrope_position_delta in this scenario, which could result in undefined behavior.

prompt_embeds is far more familiar here than M-RoPE, and it is understood that each model that supports it is responsible for defining its own get_mrope_input_positions which have varying implementations. There is insufficient knowledge to be prescriptive in how the two features should interact in the offline case, other than possibly raising a validation error earlier on preventing that combination (which would emulate the current assertion behavior). Regardless, in offline mode, the chances of a remote user being able to exploit this are slim-to-nil compared to the online case which is incredibly straightforward.

Impact

What kind of vulnerability is it? Who is impacted?

Denial of Service caused by an incorrect assertion inside of the GPUModelRunner which causes a fatal EngineCore exception
Any configuration with --enable-prompt-embeds and M-RoPE-supported model is vulnerable
The attack is extremely easy from the remote attacker's perspective (copying the official prompt_embeds online mode docs examples almost-verbatim, accounting for model-name and connection details, of course, will induce a guaranteed shutdown)

Severity

7.1 (High)


                  
                    CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "vllm"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.12.0"
            },
            {
              "fixed": "0.24.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-55514"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-617"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-07-20T19:13:07Z",
    "nvd_published_at": "2026-07-06T21:16:57Z",
    "severity": "HIGH"
  },
  "details": "### Summary\n_Short summary of the problem. Make the impact and severity as clear as possible. For example: An unsafe deserialization vulnerability allows any unauthenticated user to execute arbitrary code on the server._\n\nSending a pure prompt embeds payload in a `/v1/completions` request with a model using M-RoPE causes the EngineCore to fail an assertion and fatally crash, shutting down the entire server application.\n\nAny remote user who is authorized to make a `/v1/completions` endpoint can trivially make such a request and induce a crash.\n\n### Details\n_Give all details on the vulnerability. Pointing to the incriminated source code is very helpful for the maintainer._\n\nIn commit [56669c1](https://github.com/vllm-project/vllm/commit/56669c1f293d5c53b6a19ddf2f78802fa9fff2c2), a simple assert intended to be a type-narrowing assert was added to the `_init_mrope_positions` method in `GPUModelRunner` (the offending line on main at the time of writing: https://github.com/vllm-project/vllm/blob/2d481f8a946ee0521872af0f098674a8ee01ce4a/vllm/v1/worker/gpu_model_runner.py#L1588-L1607).\n\n```python\nassert req_state.prompt_token_ids is not None, (\n            \"M-RoPE requires prompt_token_ids to be available.\"\n        )\n```\n\nThis type narrowing assert is to prevent mypy errors later in the function because `None` is not a valid type for `mrope_model.get_mrope_input_positions`. Unfortunately, this assertion is not always true. `/v1/completions` requests that specify `prompt=None` and `prompt_embeds=\u003cnot none\u003e` will indeed create a CachedRequestState where `prompt_token_ids` is `None`. This triggers the assertion, which in turn crashes the EngineCore and the Server application.\n\n```\n(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]   File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py\", line 3997, in execute_model\n(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]     deferred_state_corrections_fn = self._update_states(scheduler_output)\n(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]   File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py\", line 1239, in _update_states\n(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]     self._init_mrope_positions(req_state)\n(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]   File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py\", line 1582, in _init_mrope_positions\n(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]     assert req_state.prompt_token_ids is not None, (\n(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167] AssertionError: M-RoPE requires prompt_token_ids to be available.\n(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] AsyncLLM output_handler failed.\n(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] Traceback (most recent call last):\n(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704]   File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/async_llm.py\", line 660, in output_handler\n(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704]     outputs = await engine_core.get_output_async()\n(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704]               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704]   File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py\", line 1030, in get_output_async\n(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704]     raise self._format_exception(outputs) from None\n(APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause.\n```\n\nAll requests using the `/v1/chat/completions` endpoints will have text/`prompt_token_ids` parts (corresponding to the chat template), and `prompt_embeds` parts are handled as mm_features. This method (rightly) filters out those `prompt_embeds` content parts as they are treated as text positions.\n\nA sufficient solution to type narrowing here without raising a fatal assertion is to instead replace the assertion with a using dummy token ids:\n\n```python\ndef _init_mrope_positions(self, req_state: CachedRequestState):\n    model = self.get_model()\n    assert supports_mrope(model), \"M-RoPE support is not implemented.\"\n    mrope_model = cast(SupportsMRoPE, model)\n\n    # Filter out prompt_embeds modality (text-only position info)\n    mrope_features = [\n        f for f in req_state.mm_features if f.modality != \"prompt_embeds\"\n    ]\n    \n    # Handle both token_ids and embeddings-only inputs\n    if req_state.prompt_token_ids is not None:\n        input_tokens = req_state.prompt_token_ids\n    elif req_state.prompt_embeds is not None:\n        # For text-only embeddings, dummy token IDs are safe since\n        # get_mrope_input_positions only uses len(input_tokens) when mm_features is empty\n        seq_len = req_state.prompt_embeds.shape[0]\n        input_tokens = list(range(seq_len))\n        # Verify no mm_features remain (should be true after prompt_embeds filter)\n        assert len(mrope_features) == 0, (\n            \"M-RoPE with prompt_embeds-only input should have no multimodal features\"\n        )\n    else:\n        raise ValueError(\n            \"M-RoPE requires either prompt_token_ids or prompt_embeds.\"\n        )\n\n    req_state.mrope_positions, req_state.mrope_position_delta = (\n        mrope_model.get_mrope_input_positions(\n            input_tokens,\n            mrope_features,\n        )\n    )\n```\n\nTechnically, in isolation, this method still crashes in the case where `req_state.prompt_token_ids is None and req_state.mm_features`, so the solution above still leaves that potential vector open. As far as can be determined, however, such a `req_state` is impossible in the first place in online mode, because it would require a `/v1/completions` request with `prompt_embeds` AND multimodal features, but the `/v1/completions` request schema does not expose multimodal inputs in any discernible way. Today, those are the only two endpoints with `prompt_embeds` support. \n\nWhen in offline mode, it *is technically* possible to directly create an `EngineCoreRequest` that has `prompt_embeds and not prompt_token_ids and mm_features`, and pass that to `LLM.generate`. That would trigger this same assertion, and no validation would prevent that combination. It is strongly suspected, though, that this combination would be undefined in any model that support M-RoPE, because it would not be possible to determine which token positions correspond to `mm_features`. The proposed solution above would end up not setting `req_state.mrope_positions` and `req_state.mrope_position_delta` in this scenario, which could result in undefined behavior.\n\n`prompt_embeds` is far more familiar here than M-RoPE, and it is understood that each model that supports it is responsible for defining its own `get_mrope_input_positions` which have varying implementations. There is insufficient knowledge to be prescriptive in how the two features should interact in the offline case, other than possibly raising a validation error earlier on preventing that combination (which would emulate the current assertion behavior). Regardless, in offline mode, the chances of a remote user being able to exploit this are slim-to-nil compared to the online case which is incredibly straightforward.\n\n### Impact\n_What kind of vulnerability is it? Who is impacted?_\n\n- Denial of Service caused by an incorrect assertion inside of the `GPUModelRunner` which causes a fatal EngineCore exception\n- Any configuration with `--enable-prompt-embeds` and M-RoPE-supported model is vulnerable\n- The attack is extremely easy from the remote attacker\u0027s perspective (copying the official `prompt_embeds` online mode docs examples almost-verbatim, accounting for model-name and connection details, of course, will induce a guaranteed shutdown)",
  "id": "GHSA-33cg-gxv8-3p8g",
  "modified": "2026-07-20T19:13:07Z",
  "published": "2026-07-20T19:13:07Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-33cg-gxv8-3p8g"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-55514"
    },
    {
      "type": "WEB",
      "url": "https://github.com/vllm-project/vllm/pull/45252"
    },
    {
      "type": "WEB",
      "url": "https://github.com/vllm-project/vllm/commit/470229c37efaf69c86e8bc97482b0b1ff7551c65"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2026-2303.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/vllm-project/vllm"
    },
    {
      "type": "WEB",
      "url": "https://github.com/vllm-project/vllm/releases/tag/v0.24.0"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "vLLM denial of service via prompt embeds on M-RoPE models"
}

GHSA-3438-W73J-W82H

Vulnerability from github – Published: 2022-05-06 00:00 – Updated: 2022-05-14 00:01

Details

In GPAC 2.1-DEV-rev87-g053aae8-master, function BS_ReadByte() in utils/bitstream.c has a failed assertion, which causes a Denial of Service. This vulnerability was fixed in commit 9ea93a2.

Severity

7.5 (High)


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

Show details on source website

JSON

To clipboard

{
  "affected": [],
  "aliases": [
    "CVE-2022-29339"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-617"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2022-05-05T13:15:00Z",
    "severity": "HIGH"
  },
  "details": "In GPAC 2.1-DEV-rev87-g053aae8-master, function BS_ReadByte() in utils/bitstream.c has a failed assertion, which causes a Denial of Service. This vulnerability was fixed in commit 9ea93a2.",
  "id": "GHSA-3438-w73j-w82h",
  "modified": "2022-05-14T00:01:25Z",
  "published": "2022-05-06T00:00:48Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-29339"
    },
    {
      "type": "WEB",
      "url": "https://github.com/gpac/gpac/issues/2165"
    },
    {
      "type": "WEB",
      "url": "https://github.com/gpac/gpac/commit/9ea93a2ec8f555ceed1ee27294cf94822f14f10f"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-34FQ-364C-3W2G

Vulnerability from github – Published: 2024-12-03 18:31 – Updated: 2024-12-03 18:31

Details

Open62541 v1.4.6 is has an assertion failure in fuzz_binary_decode, which leads to a crash.

Severity

7.5 (High)


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

Show details on source website

JSON

To clipboard

{
  "affected": [],
  "aliases": [
    "CVE-2024-53429"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-617"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2024-11-21T15:15:35Z",
    "severity": "HIGH"
  },
  "details": "Open62541 v1.4.6 is has an assertion failure in fuzz_binary_decode, which leads to a crash.",
  "id": "GHSA-34fq-364c-3w2g",
  "modified": "2024-12-03T18:31:02Z",
  "published": "2024-12-03T18:31:02Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-53429"
    },
    {
      "type": "WEB",
      "url": "https://github.com/open62541/open62541/issues/6825"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-3538-RRCV-8MRQ

Vulnerability from github – Published: 2026-05-27 15:33 – Updated: 2026-06-25 21:31

Details

In the Linux kernel, the following vulnerability has been resolved:

net: remove WARN_ON_ONCE when accessing forward path array

Although unlikely, recent support for IPIP tunnels increases chances of reaching this WARN_ON_ONCE if userspace manages to build a sufficiently long forward path.

Remove it.

Severity

5.5 (Medium)


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

Show details on source website

JSON

To clipboard

{
  "affected": [],
  "aliases": [
    "CVE-2026-45847"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-617"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-05-27T14:16:56Z",
    "severity": "MODERATE"
  },
  "details": "In the Linux kernel, the following vulnerability has been resolved:\n\nnet: remove WARN_ON_ONCE when accessing forward path array\n\nAlthough unlikely, recent support for IPIP tunnels increases chances of\nreaching this WARN_ON_ONCE if userspace manages to build a sufficiently\nlong forward path.\n\nRemove it.",
  "id": "GHSA-3538-rrcv-8mrq",
  "modified": "2026-06-25T21:31:18Z",
  "published": "2026-05-27T15:33:12Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-45847"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/008e7a7c293b30bc43e4368dac6ea3808b75a572"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/50422613185d505201167e8bdd2f2700790d5db6"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/548244c2f542aa0ad49453e9306e715a3877bc44"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/9464ca7a6e56ad1ebf48b2ad5c16871edfad10c6"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/959ea349c7e2d4edf07b6838ca7e59345fe61a08"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/a78d055ba7c31103ad02f8eceb0c452e154d2660"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/dcf9b3c90e5560339649d088836529883fb509f3"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-359G-WG43-PFV8

Vulnerability from github – Published: 2026-01-17 18:30 – Updated: 2026-02-23 09:31

Details

A vulnerability was identified in Open5GS up to 2.7.5. This vulnerability affects the function sgwc_bearer_add of the file src/sgwc/context.c. The manipulation leads to reachable assertion. The attack is possible to be carried out remotely. The exploit is publicly available and might be used. The issue report is flagged as already-fixed.

Severity

5.3 (Medium)


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

5.5 (Medium)


                  
                    CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:P

Show details on source website

JSON

To clipboard

{
  "affected": [],
  "aliases": [
    "CVE-2025-15531"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-617"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-01-17T16:16:05Z",
    "severity": "MODERATE"
  },
  "details": "A vulnerability was identified in Open5GS up to 2.7.5. This vulnerability affects the function sgwc_bearer_add of the file src/sgwc/context.c. The manipulation leads to reachable assertion. The attack is possible to be carried out remotely. The exploit is publicly available and might be used. The issue report is flagged as already-fixed.",
  "id": "GHSA-359g-wg43-pfv8",
  "modified": "2026-02-23T09:31:20Z",
  "published": "2026-01-17T18:30:19Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-15531"
    },
    {
      "type": "WEB",
      "url": "https://github.com/open5gs/open5gs/issues/4233"
    },
    {
      "type": "WEB",
      "url": "https://github.com/open5gs/open5gs/issues/4233#issue-3776216182"
    },
    {
      "type": "WEB",
      "url": "https://github.com/open5gs/open5gs"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?ctiid.341598"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?id.341598"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?submit.729339"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
      "type": "CVSS_V4"
    }
  ]
}

GHSA-35Q6-7298-9C8J

Vulnerability from github – Published: 2025-01-22 15:32 – Updated: 2025-01-29 00:31

Details

A reachable assertion in the ogs_kdf_hash_mme function of Open5GS <= 2.6.4 allows attackers to cause a Denial of Service (DoS) via a crafted NAS packet.

Severity

5.3 (Medium)


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

Show details on source website

JSON

To clipboard

{
  "affected": [],
  "aliases": [
    "CVE-2024-24432"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-617"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2025-01-22T15:15:12Z",
    "severity": "MODERATE"
  },
  "details": "A reachable assertion in the ogs_kdf_hash_mme function of Open5GS \u003c= 2.6.4 allows attackers to cause a Denial of Service (DoS) via a crafted NAS packet.",
  "id": "GHSA-35q6-7298-9c8j",
  "modified": "2025-01-29T00:31:53Z",
  "published": "2025-01-22T15:32:35Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-24432"
    },
    {
      "type": "WEB",
      "url": "https://cellularsecurity.org/ransacked"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-364J-9M4F-F6R2

Vulnerability from github – Published: 2022-05-13 01:48 – Updated: 2022-05-13 01:48

Details

In ImageMagick 7.0.5-7 Q16, an assertion failure was found in the function SetPixelChannelAttributes, which allows attackers to cause a denial of service via a crafted file.

Severity

6.5 (Medium)


                  
                    CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H

Show details on source website

JSON

To clipboard

{
  "affected": [],
  "aliases": [
    "CVE-2017-9499"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-617"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2017-06-07T14:29:00Z",
    "severity": "MODERATE"
  },
  "details": "In ImageMagick 7.0.5-7 Q16, an assertion failure was found in the function SetPixelChannelAttributes, which allows attackers to cause a denial of service via a crafted file.",
  "id": "GHSA-364j-9m4f-f6r2",
  "modified": "2022-05-13T01:48:00Z",
  "published": "2022-05-13T01:48:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2017-9499"
    },
    {
      "type": "WEB",
      "url": "https://github.com/ImageMagick/ImageMagick/issues/492"
    },
    {
      "type": "WEB",
      "url": "https://github.com/ImageMagick/ImageMagick/commit/7fd419441bc7103398e313558171d342c6315f44"
    },
    {
      "type": "WEB",
      "url": "http://www.securityfocus.com/bid/98944"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-36M4-MV4F-9C4Q

Vulnerability from github – Published: 2025-01-22 00:33 – Updated: 2025-01-23 21:31

Details

Magma versions <= 1.8.0 (fixed in v1.9 commit 08472ba98b8321f802e95f5622fa90fec2dea486) are susceptible to an assertion-based crash when an oversized NAS packet is received. An attacker may leverage this behavior to repeatedly crash the MME via either a compromised base station or via an unauthenticated cellphone within range of a base station managed by the MME, causing a denial of service.

Severity

7.5 (High)


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

Show details on source website

JSON

To clipboard

{
  "affected": [],
  "aliases": [
    "CVE-2023-37029"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-617"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2025-01-21T23:15:10Z",
    "severity": "HIGH"
  },
  "details": "Magma versions \u003c= 1.8.0 (fixed in v1.9 commit 08472ba98b8321f802e95f5622fa90fec2dea486) are susceptible to an assertion-based crash when an oversized NAS packet is received. An attacker may leverage this behavior to repeatedly crash the MME via either a compromised base station or via an unauthenticated cellphone within range of a base station managed by the MME, causing a denial of service.",
  "id": "GHSA-36m4-mv4f-9c4q",
  "modified": "2025-01-23T21:31:51Z",
  "published": "2025-01-22T00:33:37Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2023-37029"
    },
    {
      "type": "WEB",
      "url": "https://cellularsecurity.org/ransacked"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-36VM-XW34-X4PJ

Vulnerability from github – Published: 2021-05-21 14:25 – Updated: 2024-11-01 16:55

Summary

CHECK-fail in `tf.raw_ops.IRFFT`

Details

Impact

An attacker can cause a denial of service by exploiting a CHECK-failure coming from the implementation of tf.raw_ops.IRFFT:

import tensorflow as tf

values = [-10.0] * 130
values[0] = -9.999999999999995
inputs = tf.constant(values, shape=[10, 13], dtype=tf.float32)
inputs = tf.cast(inputs, dtype=tf.complex64)
fft_length = tf.constant([0], shape=[1], dtype=tf.int32)

tf.raw_ops.IRFFT(input=inputs, fft_length=fft_length)

The above example causes Eigen code to operate on an empty matrix. This triggers on an assertion and causes program termination.

Patches

We have patched the issue in GitHub commit 1c56f53be0b722ca657cbc7df461ed676c8642a2.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.

Severity

2.5 (Low)


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

2.0 (Low)


                  
                    CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.1.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.1.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.1.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29562"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-617"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T19:48:31Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nAn attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.IRFFT`:\n    \n```python\nimport tensorflow as tf\n\nvalues = [-10.0] * 130\nvalues[0] = -9.999999999999995\ninputs = tf.constant(values, shape=[10, 13], dtype=tf.float32)\ninputs = tf.cast(inputs, dtype=tf.complex64)\nfft_length = tf.constant([0], shape=[1], dtype=tf.int32)\n\ntf.raw_ops.IRFFT(input=inputs, fft_length=fft_length)\n``` \n    \nThe above example causes Eigen code to operate on an empty matrix. This triggers on an assertion and causes program termination.\n\n### Patches\nWe have patched the issue in GitHub commit [1c56f53be0b722ca657cbc7df461ed676c8642a2](https://github.com/tensorflow/tensorflow/commit/1c56f53be0b722ca657cbc7df461ed676c8642a2).\n  \nThe fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.\n\n### For more information \nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n### Attribution\nThis vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. ",
  "id": "GHSA-36vm-xw34-x4pj",
  "modified": "2024-11-01T16:55:35Z",
  "published": "2021-05-21T14:25:02Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-36vm-xw34-x4pj"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29562"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/1c56f53be0b722ca657cbc7df461ed676c8642a2"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-490.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-688.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-199.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "CHECK-fail in `tf.raw_ops.IRFFT`"
}

CWE-617

Reachable Assertion

GHSA-3242-RQGF-8X5V

GHSA-33CG-GXV8-3P8G

Summary

Details

Impact

GHSA-3438-W73J-W82H

GHSA-34FQ-364C-3W2G

GHSA-3538-RRCV-8MRQ

GHSA-359G-WG43-PFV8

GHSA-35Q6-7298-9C8J

GHSA-364J-9M4F-F6R2

GHSA-36M4-MV4F-9C4Q

GHSA-36VM-XW34-X4PJ

Impact

Patches

For more information

Attribution

Mitigation

Mitigation