104 matches found
CVE-2026-72522
libexpat before 2.8.3 has an out-of-bounds read and resultant infinite loop because low surrogates are treated the same as high surrogates during Unicode processing in the toUtf16 functions...
AZL-94698 CVE-2026-72522 affecting package expat for versions less than 2.8.3-1
libexpat before 2.8.3 has an out-of-bounds read and resultant infinite loop because low surrogates are treated the same as high surrogates during Unicode processing in the toUtf16 functions...
UBUNTU-CVE-2026-72522
libexpat before 2.8.3 has an out-of-bounds read and resultant infinite loop because low surrogates are treated the same as high surrogates during Unicode processing in the toUtf16 functions...
EUVD-2026-54990
libexpat before 2.8.3 has an out-of-bounds read and resultant infinite loop because low surrogates are treated the same as high surrogates during Unicode processing in the toUtf16 functions...
CVE-2026-72522
libexpat before 2.8.3 has an out-of-bounds read and resultant infinite loop because low surrogates are treated the same as high surrogates during Unicode processing in the toUtf16 functions...
CVE-2026-72522
libexpat before 2.8.3 has an out-of-bounds read and resultant infinite loop because low surrogates are treated the same as high surrogates during Unicode processing in the toUtf16 functions...
CVE-2026-72522
libexpat before 2.8.3 has an out-of-bounds read and resultant infinite loop because low surrogates are treated the same as high surrogates during Unicode processing in the toUtf16 functions...
PT-2026-69331
Name of the Vulnerable Software and Affected Versions libexpat versions prior to 2.8.3 Description An out-of-bounds read and a resulting infinite loop occur during Unicode processing. This happens because low surrogates are treated identically to high surrogates within the toUtf16 functions...
Reliable Remediation Impact Prediction for Black-Box Security Ratings
Security rating platforms summarize externally observable cyber exposure and are expected to help organizations prioritize remediation. A platform may want to tell an organization how a candidate remediation action would affect its score, but repeatedly exposing exact score responses can reveal...
Incorrect handling of invalid surrogate pair characters
ImpactWhat kind of vulnerability is it? Who is impacted?Anyone parsing JSON from an untrusted source is vulnerable.JSON strings that contain escaped surrogate characters not part of a proper surrogate pair were decoded incorrectly. Besides corrupting strings, this allowed for potential key...
PYSEC-2026-1057 Incorrect handling of invalid surrogate pair characters
Impact What kind of vulnerability is it? Who is impacted? Anyone parsing JSON from an untrusted source is vulnerable. JSON strings that contain escaped surrogate characters not part of a proper surrogate pair were decoded incorrectly. Besides corrupting strings, this allowed for potential key...
Formulating Subgroup Discovery As a Quantum Optimization Problem for Network Security
While current network intrusion detection systems achieve satisfactory accuracy, they often lack explainability. Subgroup Discovery SD addresses this by building interpretable rules that characterize feature interactions associated with attack traffic. With large datasets, classical heuristic bea...
ExAI5G: A Logic-Based Explainable AI Framework for Intrusion Detection in 5G Networks
Intrusion detection systems IDSs for 5G networks must handle complex, high-volume traffic. Although opaque "black-box" models can achieve high accuracy, their lack of transparency hinders trust and effective operational response. We propose ExAI5G, a framework that prioritizes interpretability by...
Not All Tokens Are Created Equal: Query-Efficient Jailbreak Fuzzing for LLMs
Large Language ModelsLLMs are widely deployed, yet are vulnerable to jailbreak prompts that elicit policy-violating outputs. Although prior studies have uncovered these risks, they typically treat all tokens as equally important during prompt mutation, overlooking the varying contributions of...
The Role of Learning in Attacking Intrusion Detection Systems
Recent work on network attacks have demonstrated that ML-based network intrusion detection systems NIDS can be evaded with adversarial perturbations. However, these attacks rely on complex optimizations that have large computational overheads, making them impractical in many real-world settings. ...
FedPoisonTTP: A Threat Model and Poisoning Attack for Federated Test-Time Personalization
Test-time personalization in federated learning enables models at clients to adjust online to local domain shifts, enhancing robustness and personalization in deployment. Yet, existing federated learning work largely overlooks the security risks that arise when local adaptation occurs at test tim...
BackWeak: Backdooring Knowledge Distillation Simply with Weak Triggers and Fine-Tuning
Knowledge Distillation KD is essential for compressing large models, yet relying on pre-trained "teacher" models downloaded from third-party repositories introduces serious security risks -- most notably backdoor attacks. Existing KD backdoor methods are typically complex and computationally...
Quantifying the Risk of Transferred Black Box Attacks
Neural networks have become pervasive across various applications, including security-related products. However, their widespread adoption has heightened concerns regarding vulnerability to adversarial attacks. With emerging regulations and standards emphasizing security, organizations must...
Black-Box Guardrail Reverse-Engineering Attack
Large language models LLMs increasingly employ guardrails to enforce ethical, legal, and application-specific constraints on their outputs. While effective at mitigating harmful responses, these guardrails introduce a new class of vulnerabilities by exposing observable decision patterns. In this...