Vulnerabilities (CVE)

Mozilla developers and community members reported memory safety bugs present in Firefox 81 and Firefox ESR 78.3. Some of these bugs showed evidence of memory corruption and we presume that with enough effort some of these could have been exploited to run arbitrary code. This vulnerability affects Firefox ESR < 78.4, Firefox < 82, and Thunderbird < 78.4.

Mozilla developers reported memory safety bugs present in Firefox 80. Some of these bugs showed evidence of memory corruption and we presume that with enough effort some of these could have been exploited to run arbitrary code. This vulnerability affects Firefox < 81.

When processing a MAR update file, after the signature has been validated, an invalid name length could result in a heap overflow, leading to memory corruption and potentially arbitrary code execution. Within Firefox as released by Mozilla, this issue is only exploitable with the Mozilla-controlled signing key. This vulnerability affects Firefox < 80.

Mozilla developers and community members reported memory safety bugs present in Firefox 78 and Firefox ESR 78.0. Some of these bugs showed evidence of memory corruption and we presume that with enough effort some of these could have been exploited to run arbitrary code. This vulnerability affects Firefox < 79, Firefox ESR < 68.11, Firefox ESR < 78.1, Thunderbird < 68.11, and Thunderbird < 78.1.

This vulnerability allows remote attackers to execute arbitrary code on affected installations of Foxit Studio Photo 3.6.6.922. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the handling of TIF files. The issue results from the lack of proper validation of user-supplied data, which can result in a write past the end of an allocated structure. An attacker can leverage this vulnera ... This vulnerability allows remote attackers to execute arbitrary code on affected installations of Foxit Studio Photo 3.6.6.922. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the handling of TIF files. The issue results from the lack of proper validation of user-supplied data, which can result in a write past the end of an allocated structure. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-10764.

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An issue was discovered in the client side of Zoho ManageEngine Desktop Central 10.0.552.W. An attacker-controlled server can trigger an integer overflow in InternetSendRequestEx and InternetSendRequestByBitrate that leads to a heap-based buffer overflow and Remote Code Execution with SYSTEM privileges. This issue will occur only when untrusted communication is initiated with server. In cloud, Agent will always connect with trusted communication.

In nDPI through 3.2, there is a stack overflow in extractRDNSequence in lib/protocols/tls.c.

ffjpeg through 2020-02-24 has a heap-based buffer overflow in jfif_decode in jfif.c.

LibRaw before 0.20-Beta3 has an out-of-bounds write in parse_exif() in metadata\exif_gps.cpp via an unrecognized AtomName and a zero value of tiff_nifds.

In SQLite before 3.32.3, select.c mishandles query-flattener optimization, leading to a multiSelectOrderBy heap overflow because of misuse of transitive properties for constant propagation.

An issue was discovered in OpenEXR before v2.5.2. Invalid chunkCount attributes could cause a heap buffer overflow in getChunkOffsetTableSize() in IlmImf/ImfMisc.cpp.

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but dep ... In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

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In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issu ... In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

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In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators ... In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code.

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In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in bot ... In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

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In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the `DCHECK` does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption. The issue is ... In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the `DCHECK` does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption. The issue is patched in commit 2d88f470dea2671b430884260f3626b1fe99830a, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

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In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856 ... In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

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In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `bat ... In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

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In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will ... In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will never be 1, hence there will be no hashmap at index 0 in `per_batch_counts`. Trying to access that in the user code results in a segmentation fault. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

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In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern. It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in a heap buffer overflow. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

A stack-based buffer overflow on DrayTek Vigor2960, Vigor3900, and Vigor300B devices before 1.5.1.1 allows remote attackers to execute arbitrary code via the formuserphonenumber parameter in an authusersms action to mainfunction.cgi.

An issue was discovered in map.c in FreedroidRPG 1.0rc2. It assumes lengths of data sets read from saved game files. It copies data from a file into a fixed-size heap-allocated buffer without size verification, leading to a heap-based buffer overflow.

Memory access out of buffer boundaries issues was discovered in Contiki-NG 4.4 through 4.5, in the SNMP BER encoder/decoder. The length of provided input/output buffers is insufficiently verified during the encoding and decoding of data. This may lead to out-of-bounds buffer read or write access in BER decoding and encoding functions.

Buffer overflows were discovered in Contiki-NG 4.4 through 4.5, in the SNMP agent. Functions parsing the OIDs in SNMP requests lack sufficient allocated target-buffer capacity verification when writing parsed OID values. The function snmp_oid_decode_oid() may overwrite memory areas beyond the provided target buffer, when called from snmp_message_decode() upon an SNMP request reception. Because the content of the write operations is externally provided in the SNMP requests, it enables a remote ov ... Buffer overflows were discovered in Contiki-NG 4.4 through 4.5, in the SNMP agent. Functions parsing the OIDs in SNMP requests lack sufficient allocated target-buffer capacity verification when writing parsed OID values. The function snmp_oid_decode_oid() may overwrite memory areas beyond the provided target buffer, when called from snmp_message_decode() upon an SNMP request reception. Because the content of the write operations is externally provided in the SNMP requests, it enables a remote overwrite of an IoT device's memory regions beyond the allocated buffer. This overflow may allow remote overwrite of stack and statically allocated variables memory regions by sending a crafted SNMP request.

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Buffer overflows were discovered in Contiki-NG 4.4 through 4.5, in the SNMP bulk get request response encoding function. The function parsing the received SNMP request does not verify the input message's requested variables against the capacity of the internal SNMP engine buffer. When a bulk get request response is assembled, a stack buffer dedicated for OIDs (with a limited capacity) is allocated in snmp_engine_get_bulk(). When snmp_engine_get_bulk() is populating the stack buffer, an overflow ... Buffer overflows were discovered in Contiki-NG 4.4 through 4.5, in the SNMP bulk get request response encoding function. The function parsing the received SNMP request does not verify the input message's requested variables against the capacity of the internal SNMP engine buffer. When a bulk get request response is assembled, a stack buffer dedicated for OIDs (with a limited capacity) is allocated in snmp_engine_get_bulk(). When snmp_engine_get_bulk() is populating the stack buffer, an overflow condition may occur due to lack of input length validation. This makes it possible to overwrite stack regions beyond the allocated buffer, including the return address from the function. As a result, the code execution path may be redirected to an address provided in the SNMP bulk get payload. If the target architecture uses common addressing space for program and data memory, it may also be possible to supply code in the SNMP request payload, and redirect the execution path to the remotely injected code, by modifying the function's return address.

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Buffer overflows were discovered in Contiki-NG 4.4 through 4.5, in the SNMP agent. The function parsing the received SNMP request does not verify the input message's requested variables against the capacity of the internal SNMP engine buffer. If the number of variables in the request exceeds the allocated buffer, a memory write out of the buffer boundaries occurs. This write operation provides a possibility to overwrite other variables allocated in the .bss section by the application. Because th ... Buffer overflows were discovered in Contiki-NG 4.4 through 4.5, in the SNMP agent. The function parsing the received SNMP request does not verify the input message's requested variables against the capacity of the internal SNMP engine buffer. If the number of variables in the request exceeds the allocated buffer, a memory write out of the buffer boundaries occurs. This write operation provides a possibility to overwrite other variables allocated in the .bss section by the application. Because the sender of the frame is in control of the content that will be written beyond the buffer limits, and there is no strict process memory separation, this issue may allow overwriting of sensitive memory areas of an IoT device.

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Softing Industrial Automation all versions prior to the latest build of version 4.47.0, The affected product is vulnerable to a heap-based buffer overflow, which may allow an attacker to remotely execute arbitrary code.

Malicious operation of the crafted web browser cookie may cause a stack-based buffer overflow in the system web server on the EDR-G902 and EDR-G903 Series Routers (versions prior to 5.4).

Delta Industrial Automation DOPSoft, Version 4.00.08.15 and prior. Opening a specially crafted project file may overflow the heap, which may allow remote code execution, disclosure/modification of information, or cause the application to crash.

Stack-based buffer overflow vulnerability in Vigor3900, Vigor2960, and Vigor300B with firmware before 1.5.1.1.

SDL (Simple DirectMedia Layer) through 2.0.12 has an Integer Overflow (and resultant SDL_memcpy heap corruption) in SDL_BlitCopy in video/SDL_blit_copy.c via a crafted .BMP file.

An issue was discovered in LibVNCServer before 0.9.13. libvncserver/rre.c allows out-of-bounds access via encodings.

An issue was discovered in LibVNCServer before 0.9.13. libvncserver/hextile.c allows out-of-bounds access via encodings.

An issue was discovered in LibVNCServer before 0.9.13. libvncserver/corre.c allows out-of-bounds access via encodings.

A buffer overflow was found in perl-DBI < 1.643 in DBI.xs. A local attacker who is able to supply a string longer than 300 characters could cause an out-of-bounds write, affecting the availability of the service or integrity of data.

A flaw was found in the Linux kernel in versions before 5.9-rc6. When changing screen size, an out-of-bounds memory write can occur leading to memory corruption or a denial of service. Due to the nature of the flaw, privilege escalation cannot be fully ruled out.

A flaw was found in the Linux kernel before 5.9-rc4. Memory corruption can be exploited to gain root privileges from unprivileged processes. The highest threat from this vulnerability is to data confidentiality and integrity.

A vulnerability was found in upstream release cryptsetup-2.2.0 where, there's a bug in LUKS2 format validation code, that is effectively invoked on every device/image presenting itself as LUKS2 container. The bug is in segments validation code in file 'lib/luks2/luks2_json_metadata.c' in function hdr_validate_segments(struct crypt_device *cd, json_object *hdr_jobj) where the code does not check for possible overflow on memory allocation used for intervals array (see statement "intervals = malloc ... A vulnerability was found in upstream release cryptsetup-2.2.0 where, there's a bug in LUKS2 format validation code, that is effectively invoked on every device/image presenting itself as LUKS2 container. The bug is in segments validation code in file 'lib/luks2/luks2_json_metadata.c' in function hdr_validate_segments(struct crypt_device *cd, json_object *hdr_jobj) where the code does not check for possible overflow on memory allocation used for intervals array (see statement "intervals = malloc(first_backup * sizeof(*intervals));"). Due to the bug, library can be *tricked* to expect such allocation was successful but for far less memory then originally expected. Later it may read data FROM image crafted by an attacker and actually write such data BEYOND allocated memory.

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An out-of-bounds read/write access flaw was found in the USB emulator of the QEMU in versions before 5.2.0. This issue occurs while processing USB packets from a guest when USBDevice 'setup_len' exceeds its 'data_buf[4096]' in the do_token_in, do_token_out routines. This flaw allows a guest user to crash the QEMU process, resulting in a denial of service, or the potential execution of arbitrary code with the privileges of the QEMU process on the host.