Malicious code in benchmark-deserialize-mu-epsilon-shell (npm)

The package benchmark-deserialize-mu-epsilon-shell was found to contain malicious code. --- Credit: OpenSSF source...

Malicious code in benchmark-epsilon-runtime-authenticate-slow (npm)

The package benchmark-epsilon-runtime-authenticate-slow was found to contain malicious code. --- Credit: OpenSSF source...

Malicious code in authorize-interpret-notify-benchmark-protected (npm)

The package authorize-interpret-notify-benchmark-protected was found to contain malicious code. --- Credit: OpenSSF source...

Malicious code in astro-benchmark (npm)

The package communicates with a domain associated with malicious activity. --- Credit: OpenSSF source...

Malicious code in array-benchmark-socket-dog-fork (npm)

Source: amazon-inspector 7b4891ee261ab5b0892b7fb36fbaf17e875b3fb4eff45655048e9333c041a850 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js, autopublish.js, autopublish2.js, autopublish3.js designed ...

Malicious Package

Overview benchmark-refactor-secure-scale-array is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization...

Malicious Package

Overview benchmark-epsilon-runtime-authenticate-slow is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that...

Malicious Package

Overview benchmark-deserialize-mu-epsilon-shell is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization...

Malicious code in alchemy-asset-transfers-benchmark (npm)

The package communicates with a domain associated with malicious activity. --- Credit: OpenSSF source...

Beyond the Payload: How User Invocation Shapes Coding Agent Vulnerability to Repository Poisoning

Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity cannot be assumed. Prior work on repository poisoning largely focuses on attacker-controlled injection and disguise, but developers also shape risk...

Same Request, Different Boundary: Evaluating Cybersecurity Assistance across Conversational Contexts

Large Language Models LLMs can solve complex problems, but their misuse in high-risk domains can lead to severe consequences. Model providers therefore restrict assistance for potentially harmful requests. Refusing all cybersecurity requests would therefore harm legitimate users. Providers need a...

LongPIBench: A Long-Context Benchmark for Prompt Injection

Prompt injection attacks pose a serious security risk to large language models in real-world applications. However, existing prompt injection benchmarks primarily focus on short-context inputs, leaving the attacks and defenses in long-context settings largely unexplored. This gap leads to a...

The Impact of Magma: A Ground-Truth Fuzzing Benchmark

Magma is an open-source and ground-truth fuzzing benchmark that enables uniform fuzzer evaluation and comparison. Magma was originally released with a research paper published at ACM SIGMETRICS 2021. This short paper explains the motivation, the design, and the impact of Magma, with a description...

FuzzingBrain-Bench V1: Evaluating Open-Ended Bug Discovery by LLMs

Evaluating the ability of large language models LLMs to discover software bugs is increasingly important. Existing benchmarks typically evaluate this capability by asking the model to generate a proof-of-concept input that triggers a predefined target vulnerability. However, this setup may overlo...

MMJailBench: A Factorized Benchmark for Disentangling Multimodal Jailbreak Vulnerabilities

Multimodal Large Language Models MLLMs are increasingly deployed in real-world applications, yet how different factors shape their jailbreak vulnerabilities remains poorly understood. Existing benchmarks often couple harmful intent, prompt framing, visual semantics, and instruction carrier within...

Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination

Machine-learning-based anomaly detection is increasingly used in industrial control systems ICS, yet most studies assume that detector training data is trustworthy. In practice, training data may be corrupted through compromised logs, labeling errors, manipulated historian records, or unsafe...

The Surprising Effectiveness of LLMs in BGP Security: Mining an Unprecedented Amount of Incidents and Boosting Anomaly Detection

Border Gateway Protocol BGP security is critical to Internet infrastructure, yet progress in routing anomaly detection has been limited by the scarcity of publicly available incident datasets, which contain only 18 recorded cases. We observe that public operator mailing lists, e.g., NANOG and...

CVE-2026-18287 Aeon load_time_series_segmentation_benchmark Code Injection Remote Code Execution Vulnerability

Aeon loadtimeseriessegmentationbenchmark Code Injection Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of aeon. User interaction is required to exploit this vulnerability in that the target must visit a malicious...

MaliciousSkillBench: A Comprehensive Benchmark for Malicious Agent Skill Detection

Agent Skills extend LLM agents with reusable instruction packages that may also include scripts, resources, and service configuration. This creates a direct distribution channel for malicious behavior, yet existing malicious-Skill datasets are fragmented across sources, artifact formats, evidence...

CVE-2026-71694

An issue in Berkeley Out-of-Order Machine BOOM / BoomTile RTL benchmark v1.2 2d08d0d8b4563212175212f9db0e69f6e68c9619 allows a remote attacker to execute arbitrary code via the CSR trap-return state restoration logic, MRET handling logic, mstatus.MPRV update path, CSRFile logic in ProcessorFuzz...