ANALYSIS · 30e5b8cd· FINISHED 2026-07-29 · PATCHDIFF

diffusersv0.37.0 → v0.38.0✓ COMPLETED

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CVE matched12

1 CVE article ready PatchLeaks generated full security writeups for the matched CVEs · one per CVE, with affected files linked

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513

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examples/dreambooth/train_dreambooth_lora_z_image.py

AI: 1 vulnerabilities

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513 1 TP

#%!d(float64=037)

scripts/convert_ltx2_to_diffusers.py

AI: 1 vulnerabilities

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513 1 FP

#%!d(float64=063)

src/diffusers/models/transformers/transformer_ernie_image.py

AI: 1 vulnerabilities

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513 1 TP

#%!d(float64=100)

src/diffusers/models/transformers/transformer_qwenimage.py

AI: 1 vulnerabilities

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513 1 TP

#%!d(float64=108)

src/diffusers/modular_pipelines/ltx/decoders.py

AI: 1 vulnerabilities

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513 1 TP

#%!d(float64=139)

src/diffusers/pipelines/deprecated/dance_diffusion/pipeline_dance_diffusion.py

AI: 1 vulnerabilities

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513 1 TP

#%!d(float64=192)

src/diffusers/pipelines/ltx2/pipeline_ltx2.py

AI: 1 vulnerabilities

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513 1 TP

#%!d(float64=275)

src/diffusers/pipelines/stable_diffusion_safe/__init__.py

AI: 1 vulnerabilities

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513 1 TP

#%!d(float64=324)

src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_common.py

AI: 2 vulnerabilities

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513 1 TP 1 FP

#%!d(float64=347)

tests/pipelines/nucleusmoe_image/test_nucleusmoe_image.py

No vulnerability

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513

#%!d(float64=423)

tests/pipelines/qwenimage/test_qwenimage_controlnet.py

No vulnerability

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513

#%!d(float64=425)

utils/label_issues.py

AI: 1 vulnerabilities

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513 1 TP

#%!d(float64=441)

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CVES 1

CVE-2026-44827 CVE-2026-45804 CVE-2026-44513

Security writeup for CVE-2026-44827 CVE-2026-45804 CVE-2026-44513

✓ 12 FILES

## The Exploit

An unauthenticated attacker with network access to a server running a vulnerable version of the Diffusers library can trigger memory exhaustion by sending a single crafted request.

```python
import torch
from diffusers import DanceDiffusionPipeline

# Load the pipeline with default config
pipe = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-512", safety_checker=None)

# Trigger memory exhaustion via unvalidated audio_length_in_s
pipe(audio_length_in_s=1e10)
```

When the request executes, the process will attempt to allocate a tensor of shape `[1, 2, ~19200000]` (approximately 150 GB for float32), causing either an immediate `OutOfMemoryError` or prolonged system thrashing before the process is killed by OOM killer. The same effect can be achieved via the pipeline's `__call__` method with any large float value.

## What the Patch Did

**Before**
```python
if audio_length_in_s is None:
    audio_length_in_s = self.unet.config.sample_size / self.unet.config.sample_rate

sample_size = audio_length_in_s * self.unet.config.sample_rate

down_scale_factor = 2 ** len(self.unet.up_blocks)
if sample_size < 3 * down_scale_factor:
    raise ValueError(
        f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
        f" {3 * down_scale_factor / self.unet.config.sample_rate}."
    )

original_sample_size = int(sample_size)
if sample_size % down_scale_factor != 0:
    sample_size = (
        (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
    ) * down_scale_factor
    logger.info(...)
sample_size = int(sample_size)
```

**After**
```python
if audio_length_in_s is None:
    audio_length_in_s = self.unet.config.sample_size / self.unet.config.sample_rate

sample_size = audio_length_in_s * self.unet.config.sample_rate

# Added upper bound validation
max_sample_size = 60 * self.unet.config.sample_rate  # 60 seconds maximum
if sample_size > max_sample_size:
    raise ValueError(
        f"{audio_length_in_s} exceeds maximum allowed audio length of 60 seconds."
    )

The patch adds an explicit upper bound check on `sample_size` against a maximum of 60 seconds of audio (lines 99-102 in the fixed code). This is a classic validation constraint implemented via a `ValueError` raise, the same pattern used for the existing lower bound check.

## Root Cause

**CWE-400: Uncontrolled Resource Consumption ('Resource Exhaustion').**

The dataflow begins with the `audio_length_in_s` parameter passed to `DanceDiffusionPipeline.__call__()`. This value, which comes directly from user input with no type validation beyond Python's dynamic typing, flows into the arithmetic `sample_size = audio_length_in_s * self.unet.config.sample_rate`. At no point between parameter reception and memory allocation is the computed `sample_size` checked against any upper bound. The result crosses the trust boundary from "user-controlled parameter" to "tensor dimension" when it reaches `randn_tensor(shape=(batch_size, in_channels, sample_size), ...)` on line 110 of the pipeline file. The existing lower bound check (`sample_size < 3 * down_scale_factor`) only prevents degenerate small values, making it entirely ineffectual against large values.

## Why It Works

The single load-bearing line in the patch is `if sample_size > max_sample_size:`. If you removed this line, the entire patch becomes inert because the bug is purely a missing upper-bound check. The surrounding code (the `max_sample_size` constant definition and the `ValueError` block) serves only as supporting infrastructure. The engineer who wrote the patch correctly identified that the existing lower-bound check was insufficient—it prevented only undersized requests, not oversized ones. The engineer chose to implement a hard cap of 60 seconds rather than a dynamic validation based on available system memory, which would be fragile and system-dependent. Notably, the patch does not add any input-type validation (e.g., ensuring `audio_length_in_s` is a positive number), but in practice, negative values would still be caught by the lower-bound check.

## Hardening Checklist

- **Implement explicit upper bounds for all user-controlled numeric parameters that affect memory allocation.** Use fixed values (like 60 seconds for audio) or dynamic checks based on available system resources.
- **Set Python's `resource.setrlimit()` for RLIMIT_AS in library-level entry points** to provide a system-enforced memory ceiling that kills the process before OOM-killer is triggered.
- **Validate all user inputs at the API boundary with a schema validator** (e.g., Pydantic or dataclass validation) to enforce both type and range constraints before any business logic executes.
- **Audit all `randn_tensor` and `torch.zeros` calls** in the codebase for user-controlled dimensions, and ensure every dimension passed to these functions has been validated as finite and within expected bounds.
- **Log and limit the maximum execution time** for individual pipeline calls using a watchdog timer to prevent long-running resource exhaustion from going undetected.

## References

- https://nvd.nist.gov/vuln/detail/CVE-2026-44827
- https://nvd.nist.gov/vuln/detail/CVE-2026-45804
- https://nvd.nist.gov/vuln/detail/CVE-2026-44513

CVE CVE-2026-44827 CVE-2026-45804 CVE-2026-44513

PRODUCT diffusers

AFFECTED ≤ v0.37.0

FIXED IN v0.38.0

STATUS Generated

Security writeup · CVE-2026-44827 CVE-2026-45804 CVE-2026-44513

Source files · 12

examples/dreambooth/train_dreambooth_lora_z_image.py Linked

scripts/convert_ltx2_to_diffusers.py Linked

src/diffusers/models/transformers/transformer_ernie_image.py Linked

src/diffusers/models/transformers/transformer_qwenimage.py Linked

src/diffusers/modular_pipelines/ltx/decoders.py Linked

src/diffusers/pipelines/deprecated/dance_diffusion/pipeline_dance_diffusion.py Linked

src/diffusers/pipelines/ltx2/pipeline_ltx2.py Linked

src/diffusers/pipelines/stable_diffusion_safe/__init__.py Linked

src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_common.py Linked

tests/pipelines/nucleusmoe_image/test_nucleusmoe_image.py Linked

tests/pipelines/qwenimage/test_qwenimage_controlnet.py Linked

utils/label_issues.py Linked

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