A Hybrid Approach to Malware Detection: Integrating Few-Shot Model-Agnostic Meta-Learning with Autoencoders

Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors. These attacks are typically launched through phishing emails, malicious downloads, or exploitation of software vulnerabilities to gain system access. Once inside, the...

fast-tts (0.0.3), few-shot-priming (>=0.0.14, <=0.0.226) potentially affected by CVE-2026-9856 via transformers (4.19.0)

transformers PYPI version 4.19.0 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - fast-tts 0.0.3 - few-shot-priming =0.0.14, =0.0.226 Source cves: CVE-2026-9856 Source advisory: OSV:PYSEC-2026-3929...

fastnn (0.4.0), few-shot-learning-nlp (>=0.0.5, <=1.0.4) +9 more potentially affected by CVE-2026-69112 via accelerate (0.24.1)

accelerate PYPI version 0.24.1 is affected by a known vulnerability. The following packages have a transitive dependency on accelerate and may be impacted: - fastnn 0.4.0 - few-shot-learning-nlp =0.0.5, =0.0.1a5, =3.4.0, =0.1.8, =0.2.25, =0.1.0, =0.1.2, =0.7.0, =0.10.0 Source cves: CVE-2026-69112...

Few-Shot Learning for Network Intrusion Detection: Methods, Datasets, and Performance

Anomaly-based network intrusion detection systems NIDS are an important first line of defense. However, training NIDS for new attack types is challenging, because labeled attack data are rarely available. Few-shot learning FSL addresses this problem by learning from few samples. However, the...

Adapter-Based Few-Shot Continual Learning for Malicious Packet Recognition

The continual evolution of malware variants necessitates detection systems that can adapt to new threats without retraining from scratch. However, continually updating models on new data often leads to catastrophic forgetting, where previously learned knowledge is overwritten. While continual...

fast-tts (0.0.3), few-shot-priming (>=0.0.14, <=0.0.226) potentially affected by CVE-2026-9856 via transformers (4.19.0)

transformers PYPI version 4.19.0 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - fast-tts 0.0.3 - few-shot-priming =0.0.14, =0.0.226 Source cves: CVE-2026-9856 Source advisory: OSV:GHSA-XRQW-3RRV-VX5W...

HilEnT: Hilbert, Entropy Transformed Image Based Malware Detection

With the increasing threat of malware across various software related domains, malware detection and classification is critical to determine the response actions. Different strategies have been adopted to address the challenge of malware detection. With the advent of deep learning techniques,...

Knowledge over Parameters: Evolving Smart Contract Vulnerability Detection

Smart contract vulnerabilities are predominantly logic bugs whose detection requires structured, step-by-step procedural knowledge of attack patterns and contract semantics. Existing LLM-based methods struggle to generate this knowledge automatically: prompt-based methods rely on manually crafted...

CrypFormBench: Benchmarking Formal Analysis Capability of Large Language Models for Cryptographic Schemes

Manual formal analysis of cryptographic schemes is labor-intensive and requires substantial expertise. While model-checking tools e.g., Scyther and Tamarin and computational-security tools e.g., CryptoVerif and EasyCrypt improve the automation of security proofs, they still rely on experts to...

fast-tts (0.0.3), few-shot-priming (>=0.0.14, <=0.0.226) potentially affected by CVE-2026-5241 via transformers (4.19.0)

transformers PYPI version 4.19.0 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - fast-tts 0.0.3 - few-shot-priming =0.0.14, =0.0.226 Source cves: CVE-2026-5241 Source advisory: OSV:GHSA-FGCW-684Q-JJ6R...

fast-tts (0.0.3), few-shot-priming (>=0.0.14, <=0.0.226) potentially affected by CVE-2026-5241 via transformers (4.19.0)

transformers PYPI version 4.19.0 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - fast-tts 0.0.3 - few-shot-priming =0.0.14, =0.0.226 Source cves: CVE-2026-5241 Source advisory: OSV:PYSEC-2026-2290...

GETA: Generalized Encrypted Traffic Analysis

Traditional traffic analysis is being fundamentally challenged by the rapid adoption of encryption, tunnelling, and privacy-preserving protocols, which increasingly obscure packet payloads and limit the usefulness of Deep Packet Inspection DPI. Although machine learning has advanced encrypted...

fast-tts (0.0.3), few-shot-priming (>=0.0.14, <=0.0.226) potentially affected by CVE-2026-4372 via transformers (4.19.0)

transformers PYPI version 4.19.0 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - fast-tts 0.0.3 - few-shot-priming =0.0.14, =0.0.226 Source cves: CVE-2026-4372 Source advisory: OSV:GHSA-29PF-2H5F-8G72...

Involuntary In-Context Learning: Exploiting Few-Shot Pattern Completion to Bypass Safety Alignment in GPT-5.4

Safety alignment in large language models relies on behavioral training that can be overridden when sufficiently strong in-context patterns compete with learned refusal behaviors. We introduce Involuntary In-Context Learning IICL, an attack class that uses abstract operator framing with few-shot...

fast-tts (0.0.3), few-shot-priming (>=0.0.14, <=0.0.226) potentially affected by CVE-2026-1839 via transformers (4.19.0)

transformers PYPI version 4.19.0 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - fast-tts 0.0.3 - few-shot-priming =0.0.14, =0.0.226 Source cves: CVE-2026-1839 Source advisory: OSV:GHSA-69W3-R845-3855...

From Incomplete Architecture to Quantified Risk: Multimodal LLM-Driven Security Assessment for Cyber-Physical Systems

Cyber-physical systems often contend with incomplete architectural documentation or outdated information resulting from legacy technologies, knowledge management gaps, and the complexity of integrating diverse subsystems over extended operational lifecycles. This architectural incompleteness...

Benchmarking Large Language Models for Zero-Shot and Few-Shot Phishing URL Detection

The Uniform Resource Locator URL, introduced in a connectivity-first era to define access and locate resources, remains historically limited, lacking future-proof mechanisms for security, trust, or resilience against fraud and abuse, despite the introduction of reactive protections like HTTPS...

Rethinking On-Device LLM Reasoning: Why Analogical Mapping Outperforms Abstract Thinking for IoT DDoS Detection

The rapid expansion of IoT deployments has intensified cybersecurity threats, notably Distributed Denial of Service DDoS attacks, characterized by increasingly sophisticated patterns. Leveraging Generative AI through On-Device Large Language Models ODLLMs provides a viable solution for real-time...

Few-Shot Learning for Security Bug Report Identification

Security bug reports require prompt identification to minimize the window of vulnerability in software systems. Traditional machine learning ML techniques for classifying bug reports to identify security bug reports rely heavily on large amounts of labeled data. However, datasets for security bug...

COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers

This paper studies how multimodal large language models MLLMs undermine the security guarantees of visual CAPTCHA. We identify the attack surface where an adversary can cheaply automate CAPTCHA solving using off-the-shelf models. We evaluate 7 leading commercial and open-source MLLMs across 18...