TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.io.decode_raw produces incorrect results and crashes the Python interpreter when combining fixed_length and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the fixed_length value to the size of the type argument. The fixed_length argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the out_data pointer by fixed_length * sizeof(T) bytes whereas it only copied at most fixed_length bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory. 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.
The product writes data past the end, or before the beginning, of the intended buffer.
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Tensorflow | * | 2.1.4 (excluding) | |
| Tensorflow | 2.2.0 (including) | 2.2.3 (excluding) | |
| Tensorflow | 2.3.0 (including) | 2.3.3 (excluding) | |
| Tensorflow | 2.4.0 (including) | 2.4.2 (excluding) |
Use a language that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid.
For example, many languages that perform their own memory management, such as Java and Perl, are not subject to buffer overflows. Other languages, such as Ada and C#, typically provide overflow protection, but the protection can be disabled by the programmer.
Be wary that a language’s interface to native code may still be subject to overflows, even if the language itself is theoretically safe.
Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid.
Examples include the Safe C String Library (SafeStr) by Messier and Viega [REF-57], and the Strsafe.h library from Microsoft [REF-56]. These libraries provide safer versions of overflow-prone string-handling functions.
Use automatic buffer overflow detection mechanisms that are offered by certain compilers or compiler extensions. Examples include: the Microsoft Visual Studio /GS flag, Fedora/Red Hat FORTIFY_SOURCE GCC flag, StackGuard, and ProPolice, which provide various mechanisms including canary-based detection and range/index checking.
D3-SFCV (Stack Frame Canary Validation) from D3FEND [REF-1334] discusses canary-based detection in detail.
Consider adhering to the following rules when allocating and managing an application’s memory:
Run or compile the software using features or extensions that randomly arrange the positions of a program’s executable and libraries in memory. Because this makes the addresses unpredictable, it can prevent an attacker from reliably jumping to exploitable code.
Examples include Address Space Layout Randomization (ASLR) [REF-58] [REF-60] and Position-Independent Executables (PIE) [REF-64]. Imported modules may be similarly realigned if their default memory addresses conflict with other modules, in a process known as “rebasing” (for Windows) and “prelinking” (for Linux) [REF-1332] using randomly generated addresses. ASLR for libraries cannot be used in conjunction with prelink since it would require relocating the libraries at run-time, defeating the whole purpose of prelinking.
For more information on these techniques see D3-SAOR (Segment Address Offset Randomization) from D3FEND [REF-1335].
Use a CPU and operating system that offers Data Execution Protection (using hardware NX or XD bits) or the equivalent techniques that simulate this feature in software, such as PaX [REF-60] [REF-61]. These techniques ensure that any instruction executed is exclusively at a memory address that is part of the code segment.
For more information on these techniques see D3-PSEP (Process Segment Execution Prevention) from D3FEND [REF-1336].