tract-nnef: integer overflow in NNEF `.dat` tensor parser yields an out-of-bounds read on model load
- When
- Where
- Global (internet)
- Category
- cyber_advisory · rust
- **Component:** `tract-nnef` (`nnef/src/tensors.rs::read_tensor`) + `tract-data` (`data/src/tensor.rs`) - **Affected versions:** `< 0.21.16`, `0.22.0`–`0.22.2`, `0.23.0`–`0.23.1` — the dense `DatLoader` path was unguarded across all three release lines; patched in 0.21.16 / 0.22.2 / 0.23.1 - **Class:** CWE-190 (integer overflow) → CWE-125 (out-of-bounds read) - **Trigger:** loading a crafted NNEF model archive (`*.nnef.tgz` / `*.nnef.tar` / dir) via the public `tract_nnef::nnef().model_for_path` / `model_for_read` - **Impact:** `read_tensor` returns a memory-unsafe tensor (reported `len` 2^61 over a 56-byte heap allocation). Always-on primitive: a **bounded heap out-of-bounds read** during model build (`as_uniform`), an adjacent-heap information-disclosure reachable via the public load API. The resulting slice is an unsound `from_raw_parts(ptr, 2^61)` that **SIGSEGVs (DoS)** on any access past the mapped region (demonstrated by direct access). No out-of-bounds write and no RCE were achieved — tract's const-folding/`as_uniform` fast-paths fold simple consuming graphs without the full read. - **Severity:** Medium ## Summary `read_tensor` builds a tensor `shape` from attacker-controlled 32-bit dimensions and computes the element count `len = product(shape)` and the byte allocation `product(shape) * size_of(dt)` with **unchecked `usize` arithmetic**. In `--release` (no `overflow-checks`), both products wrap modulo 2^64. An attacker chooses dimensions so that the wrapped products collapse to a small value that satisfies the header consistency check, while the *true* element count remains astronomically large. `read_tensor` returns `Ok` with a `Tensor` whose reported `len` (e.g. 2^61+7) is far larger than its backing heap allocation (e.g. 56 bytes). The unchecked slice accessor `as_slice_unchecked` (`from_raw_parts(ptr, self.len)`) then produces a slice spanning ~18 exabytes over a 56-byte buffer. The out-of-bounds read fires automatically during model build (no inference required), reachable through the default `DatLoader` resource loader. ## Root cause `nnef/src/tensors.rs`, `read_tensor`: ``` let shape: TVec<usize> = header.dims[0..header.rank as usize].iter().map(|d| *d as _).collect(); let len = shape.iter().product::<usize>(); // (1) unchecked, wraps ... } else if header.bits_per_item != u32::MAX && len * (header.bits_per_item as usize / 8) != header.data_size_bytes as usize // (2) wrapped == u32 { bail!(...); } ... let mut tensor = unsafe { Tensor::uninitialized_dt(dt, &shape)? }; // (3) alloc off the same wrapped product ... reader.read_exact(plain.as_bytes_mut())?; // storage-bounded read, no overflow here Ok(tensor) ``` `data/src/tensor.rs`, `uninitialized_aligned_dt`: ``` let bytes = shape.iter().cloned().product::<usize>() * dt.size_of(); // (3) wraps to the same small value let storage = ... Blob::new_for_size_and_align(bytes, alignment) ...; ... tensor.update_strides_and_len(); // len = product(shape), wraps, no clamp ``` The three quantities — the consistency-check LHS `(2)`, the allocation `(3)`, and the reported `len` — are all the same wrapped `product(shape)*size_of`, so they stay mutually consistent and **the consistency check at `(2)` cannot catch the overflow**. `data_size_bytes` is a `u32`, so the attacker simply sets it to the wrapped value. Corruption sink — `data/src/tensor.rs::as_slice_unchecked` (and `data/src/tensor/plain_view.rs::as_slice_unchecked`): ``` if self.storage.byte_len() == 0 { &[] } else { std::slice::from_raw_parts(self.as_ptr_unchecked(), self.len()) } // len = 2^61 over a 56-byte alloc ``` The only guard is `byte_len() == 0`. A small **non-zero** allocation defeats it and yields an unsound oversized slice. ## Witness (F64) ``` dims = [33955849, 7005787, 359, 3, 3, 3] (rank 6, each <= u32::MAX) product(shape)= 2_305_843_009_213_693_959 = 2^61 + 7 bits_per_item = 64 (F64), item_type = 0, item_type_vendor = 0 data_size_bytes = 56 # == (2^61+7)*8 mod 2^64 ``` - `len * (bits/8) mod 2^64 = (2^61+7)*8 mod 2^64 = 56 == data_size_bytes` → consistency check passes. - allocation = `(2^61+7)*8 mod 2^64 = 56` bytes (7 × F64). - reported `len` = `2^61+7` elements. Only the `is_copy()` numeric arms (F16/F32/F64/int, and likely the `complex` arms) are exploitable. F64 is the cleanest (`bits/8` divides evenly). The `bool`, `String`, and block-quant paths are each guarded by an independent mechanism (size_of==1 prevents byte/element divergence; `String` bails on a missing `num_traits::Zero` impl; block-quant has its own `ensure!(expected_len == data_size_bytes)` and uses non-plain `Exotic` storage). ## Reachability (load-time, public API) ``` nnef().model_for_read(tar) -> proto_model_for_read nnef/src/framework.rs:303 -> DatLoader.try_load (any *.dat) nnef/src/resource.rs:97 (default loader, framework.rs:33) -> read_tensor -> Ok(Tensor{len=2^61+7, storage=56B}) nnef/src/tensors.rs:61 -> into_typed_model -> variable() fragment nnef/src/ops/nnef/deser.rs:74 ensure!(tensor.shape() == &*shape) deser.rs:122 (attacker matches shape in graph.nnef -> passes) -> Const::new -> wire_node core/src/model/typed.rs:67 -> Const::output_facts core/src/ops/konst.rs:54 -> TypedFact::try_from core/src/model/fact.rs:459 -> Tensor::as_uniform -> is_uniform_t::<f64> data/src/tensor.rs:1099 -> as_slice_unchecked::<f64> data/src/tensor.rs:1044 -> from_raw_parts(ptr, 2^61+7) over 56-byte buffer -> OOB READ ``` No shape-vs-storage re-validation exists anywhere on this path (`proto.validate()` checks only the AST; `Const::new` checks only `is_plain`; `check_for_access` checks only the datum type; even the *safe* `PlainView::as_slice` does `from_raw_parts(ptr, self.len)` with no length guard). ## Execution (proof of concept) Reproduced against the crate at the affected revision, `--release`, x86_64-linux. Three scenarios: 1. **Direct `read_tensor`** — feed the crafted 128-byte header + 56-byte payload: - `read_tensor -> Ok`, `shape=[33955849,7005787,359,3,3,3]`, `len()=2305843009213693959`, `as_bytes().len()=56`, `as_slice::<f64>().len()=2305843009213693959`. - `s[7]` (first element past the 56-byte allocation) returns `0x0000000000000041` → **heap OOB read** (adjacent-heap disclosure). - `s[1<<40]` → **SIGSEGV** (signal 11). 2. **Public load API** — build a malicious `.nnef.tar` (`graph.nnef` with `variable(label='weights', shape=[...])` + `weights.dat`) and call `nnef().model_for_read()`: - returns `Ok` with one `Const` node, `out[0].fact.uniform=Some(...)`, `len()=2305843009213693959` over a 56-byte buffer → confirms `as_uniform`/`is_uniform_t`/`as_slice_unchecked` performed an **OOB read on load** (bounded over-read here because `is_uniform`'s `.all()` short-circuits on the uniform `0x41` payload). 3. **Optimized graph** — same archive but the const is consumed (`output = mul(weights, weights)`), then `into_optimized` / `run`: - **Does not crash.** With both a uniform (`0x41×56`) and a non-uniform (`0..56`) payload, `into_optimized` const-folds `mul(const, const)` to a single node **without a full-length materialization** of the oversized const, and `run` completes. A reliable arbitrary-length crash through a *normal optimized graph* was therefore NOT demonstrated; the always-on primitive is the bounded load-time over-read (scenario 2), and the wild-slice SIGSEGV is shown via direct access (scenario 1). Runnable PoC sources are available to the maintainers on request. ## Detection - **Static:** flag `*.iter().product::<usize>()` over externally-controlled dimensions without `checked_*`/`try_into`, especially when the result feeds an allocation and a separately-tracked `len`. - **Runtime / fleet:** crash telem
Sources
- GitHub Advisory Database ↗ · first seen 2026-06-18 15:05 UTC
Defaxon links out to the original reporting and never republishes article text.
Correlated events
Computed by the Defaxon correlation engine — linked by shared actors, co-location, and temporal proximity. Scored hypotheses, never causal claims.
No correlated events found in the current window. As more events arrive, connections form automatically.