brace-expansion: Brace-expansion: Denial of Service due to exponential-time complexity

A flaw was found in brace-expansion. An attacker can exploit a vulnerability in the expand function by providing a specially crafted string. This string, containing consecutive non-expanding brace groups, can trigger exponential-time complexity, leading to significant CPU consumption and event-lo...

CVE-2026-67216

cJSON through 1.7.19 contains an inefficient algorithmic complexity flaw in cJSONCompare. When comparing objects, the function recurses into each shared subtree twice, once in each direction, with no depth guard, making the running time exponential in nesting depth. A small, deeply nested documen...

CVE-2026-6879

Element.findall and fully-consumed Element.iterfind exhibit On^2 time complexity when using XPath index predicates e.g. 1, last, last-N on XML documents with many same-tag siblings. Element.find is only affected when the first match is near the end of the sibling list, such as with last or...

brace-expansion: Brace-expansion: Denial of Service due to exponential-time complexity

A flaw was found in brace-expansion. An attacker can exploit a vulnerability in the expand function by providing a specially crafted string. This string, containing consecutive non-expanding brace groups, can trigger exponential-time complexity, leading to significant CPU consumption and event-lo...

PT-2026-63284

Name of the Vulnerable Software and Affected Versions Gitea affected versions not specified Description An authenticated user can cause a denial of service by submitting a specially crafted RFC 4716 SSH2 public key to the SSH key ingestion endpoint. The issue stems from a normalization function...

CVE-2026-59887

linkify-it is a links recognition library with full Unicode support. Prior to 5.0.2, the mailto: schema validator used by .test and .match can be invoked at every mailto: occurrence and scan the remaining input through srcemailname in lib/re.mjs, causing On^2 CPU consumption on crafted user text...

PYSEC-2026-2022 phi4mm: Quadratic Time Complexity in Input Token Processing​ leads to denial of service

Summary A critical performance vulnerability has been identified in the input preprocessing logic of the multimodal tokenizer. The code dynamically replaces placeholder tokens e.g., , with repeated tokens based on precomputed lengths. Due to ​​inefficient list concatenation operations​​, the...

CVE-2026-13311 shell-quote parse() is quadratic in token count, enabling denial of service

shell-quote prior to 1.8.5 finalizes parsed tokens in parse using Array.prototype.concat as a reduce accumulator, which reallocates and copies the entire growing array on every iteration. As a result parse runs in On^2 time relative to the number of input tokens. An attacker who can supply an...

EUVD-2021-1748

Malware in sbrugna...

EUVD-2021-1380

Malware in sbrugna...

EUVD-2021-0725

Malware in sbrugna...

EUVD-2021-0928

Malware in sbrugna...

EUVD-2020-3159

Malware in sbrugna...

EUVD-2025-12612

Malicious code in bioql PyPI...

EUVD-2023-28818

Malicious code in bioql PyPI...

EUVD-2023-30291

Malicious code in bioql PyPI...

EUVD-2023-26630

Malicious code in bioql PyPI...

Linux Distros Unpatched Vulnerability : CVE-2021-33587

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - The css-what package 4.0.0 through 5.0.0 for Node.js does not ensure that attribute parsing has Linear Time Complexity relative to the size of the input...

BIT-LIBPYTHON-2020-10735

A flaw was found in python. In algorithms with quadratic time complexity using non-binary bases, when using int"text", a system could take 50ms to parse an int string with 100,000 digits and 5s for 1,000,000 digits float, decimal, int.frombytes, and int for binary bases 2, 4, 8, 16, and 32 are no...

Outsourced Privacy-Preserving Feature Selection Based on Fully Homomorphic Encryption

Feature selection is a technique that extracts a meaningful subset from a set of features in training data. When the training data is large-scale, appropriate feature selection enables the removal of redundant features, which can improve generalization performance, accelerate the training process...