Abstract: A communication method and apparatus are provided. The method includes: A first apparatus may receive configuration information, where the configuration information is used to configure at least one time period for performing measurement. The first apparatus may determine a remaining delay for transmitting first data, and transmit the first data and/or perform measurement in a first time period based on the remaining delay. The first time period may be an overlapping part of a second time period and a third time period, the second time period is one of the at least one time period, the third time period is a time period corresponding to a time domain position of a first resource, and the first resource is a scheduled or configured resource used for transmitting the first data.

Abstract: A prompt injection detection system intercepts queries to a generative artificial intelligence (AI) system and generates a response to the query with a first large language model (LLM). A second LLM is then instructed to determine a number of intents of the query, a true question the query is asking, whether the response is responsive to the true question the query is asking, and an explanation for the response being responsive or non-responsive to the true question. If the response is non-responsive to the true question of the query, the detection system blocks the query from being communicated/input to the generative AI system and flags the query as potentially being associated with a token smuggling prompt injection attack.

Abstract: An indication of a potential network threat and one or more network entries associated with the potential network threat are received. One or more example network exploit content associated with the potential network threat are identified. At least a portion of the one or more network entries and at least a portion of the identified one or more example network exploit content are analyzed using a large language model to determine a large language model output associated with whether the potential network threat is a false positive threat. The large language model output is utilized to provide a computer security analysis result.

Abstract: A security feed normalizer aggregates and normalizes threat intelligence data across security feeds and extracts threat descriptors of cybersecurity threats from the aggregated/normalized data. A first large language model (LLM) determines whether each threat descriptor is informative, i.e., comprises sufficient information for reproducing/generating network traffic of the corresponding cybersecurity threat. For informative threat descriptors, a second LLM generates network traffic for the corresponding cybersecurity threats. The generated network traffic is used for subsequent remediation of corresponding threats.

Abstract: Detection of command and control malware is disclosed. A network traffic session is monitored. Automatic feature identification for real-time malicious command and control traffic detection based on a request header of the monitored network traffic session using a deep learning model is performed.

Abstract: This application provides a communication method and apparatus based on a configured grant configuration and an inter-frequency measurement configuration, to reduce a service transmission delay when an SPS transmission occasion collides and/or a CG transmission occasion collides with an MG. In this method, an access network device may send first configuration information and second configuration information to a terminal. The first configuration information is used to configure a first CG transmission occasion and a first time offset, and the second configuration information is used to configure a measurement occasion for inter-frequency measurement. The terminal and the access network device can perform transmission of uplink data on a second CG transmission occasion when the first CG transmission occasion overlaps the measurement occasion in time domain.

Abstract: A modulation and coding scheme configuration-based communication method and an apparatus are provided. The method includes: a first communication apparatus receives first information, where the first information may indicate a first resource and a second resource, and the first information may further indicate a first modulation and coding scheme (MCS) corresponding to the first resource and a second MCS corresponding to the second resource; and then, the first communication apparatus determines, based on a data volume of first data, to use the first resource and the first MCS to transmit the first data, or to use the second resource and the second MCS to transmit the first data. The first information indicates the first resource and the second resource, and indicates the first MCS corresponding to the first resource and the second MCS corresponding to the second resource.

Abstract: A quality of service-based data transmission method and an apparatus are provided, so that a terminal can better support QoS-based data transmission, thereby improving service performance and user experience.

Abstract: This application provides a measurement method and an apparatus. The method includes: receiving first configuration information, where the first configuration information includes first information and second information, the first information indicates a length of each of at least two first measurement gaps, the at least two first measurement gaps are in a first measurement period, there is an interval in time domain between two adjacent first measurement gaps among the at least two first measurement gaps, and the second information indicates a start time point of each of the at least two first measurement gaps; and performing inter-frequency measurement in the at least two first measurement gaps.

Abstract: This application relates to a communication method and apparatus, and a system. In one example method, a terminal apparatus may perform inter-frequency measurement in a first measurement gap with shorter duration and/or a longer period based on sending of first information used to request a resource or indicating that the terminal apparatus has data to be transmitted. Additionally or alternatively, a terminal apparatus may stop performing inter-frequency measurement in a measurement gap based on sending of first information.

Abstract: A testing framework conducts a dialogue with a multi-modal foundation model to determine how to interact with an application via a user interaction emulator tool based on images captured from the UI of the application. Initially, the testing framework will capture an image of the application UI. The testing framework then prompts the foundation model to explain how to interact with the application to achieve a function of the application given the captured image. The testing framework derives commands from the model response for running the user interaction emulator tool and captures an image of the application UI after the interaction and again prompts the foundation model to determine whether the function was successfully performed or to explain a failure. The testing framework continues the dialogue with successive prompts for interaction with the application informed by the explanation of preceding failure.

Abstract: A system, method, and device for detecting Command and Control (C2) traffic is disclosed. The method includes (i) converting, by one or more processors, a header for network traffic to a header representation having a smaller dimensionality than the header, (ii) querying a classifier based at least in part on the header representation to obtain a traffic classification, (iii) automatically detecting C2 traffic based at least in part on the traffic classification, and (iv) handling the network traffic based at least in part on the traffic classification.

Abstract: Various techniques for LLM powered detection reasoning solutions are disclosed. In some embodiments, a system, a process, and/or a computer program product for an LLM powered detection reasoning solution includes monitoring network traffic at a security platform, wherein the security platform generates a sample based on the monitored network traffic; sending the sample to a security service to generate a Large Language Model (LLM) powered detection and reason, wherein the LLM is prompted to automatically generate a malware or benign verdict and a reason for explaining the verdict; and reporting the LLM powered detection and reason.

Abstract: A physical downlink control channel (PDCCH) monitoring method is used to reduce power consumption of a terminal device. The method includes obtaining a first discontinuous reception (DRX) parameter used to configure a first DRX and a second DRX parameter used to configure a second DRX. A start time of a first cycle period of the first DRX is t1, a start time of a second cycle period of the second DRX is t2, and t1 is earlier than t2. When a time difference between t1 and t2 is less than a first threshold, PDCCH monitoring is initiated at a time t3, where t3 is no later than t2 and t3 is later than t1. This enables the terminal device to delay the start time of the first cycle period, reducing the time of PDCCH monitoring, thereby reducing power consumption of the terminal device.

Abstract: A cybersecurity intelligence system maintains a current view of published cybersecurity issues by periodically crawling websites that publish information about cybersecurity issues, including first impression issues and updates to issues. The system aggregates unstructured descriptions for a same issue. For each published issue detected from crawling, the system prompts a foundation model to generate a structured representation that can be consumed. The structured representation includes key-value pairs corresponding to issue type and affected vendor product. Distilling and consolidating the issue descriptions into these informational components intelligently surfaces salient features of the information for addressing issues. After some quality assurance of the structured representations from the model, the structured representations are published for access by consumers.

Abstract: Techniques for inline exploit detection via loose condition forwarding and cloud analysis (e.g., cloud-based security analysis) are disclosed. In some embodiments, a system/process/computer program product for inline exploit detection via loose condition forwarding and cloud analysis includes monitoring network traffic of a session at a security platform; selecting a subset of the monitored network traffic associated with the session to send to a cloud-based security service for analysis based on a security policy, wherein the selected subset of the monitored network traffic is determined to be associated with suspicious session traffic that is selected using a prefilter condition (e.g., a prefilter signature, heuristics, machine learning implemented prefiltering condition, etc.) for an unknown exploit; and receiving, from the cloud-based security service, results of the analysis based on the security policy, and performing a responsive action based on the results of the analysis based on the security policy.

Abstract: A security feed normalizer aggregates and normalizes threat intelligence data across security feeds and extracts threat descriptors of cybersecurity threats from the aggregated/normalized data. A first large language model (LLM) determines whether each threat descriptor is informative, i.e., comprises sufficient information for reproducing/generating network traffic of the corresponding cybersecurity threat. For informative threat descriptors, a second LLM generates network traffic for the corresponding cybersecurity threats. The generated network traffic is used for subsequent remediation of corresponding threats.

Abstract: A system facilitates multi-session, multi-aspect analysis of conversations between users and applications that leverage language models (“generative AI applications”). The system retrieves conversation logs for conversations carried out between users and a generative AI application(s) and sorts the conversation logs by conversation. The system analyzes the conversations in multiple aspects for various use cases: security, analytics, and organizational compliance. In the security analysis, the system analyzes the queries and responses of each conversation to determine if the conversation includes a multi-turn attack. For the analytics and organization compliance use cases, the system evaluates how end users are using the generative AI application(s) across conversations to provide customers with additional insights, such as the spread of subject matter areas for which employees are using the generative AI application(s).

Abstract: A genetic algorithm is implemented to generate prompts that evade content filters of generative artificial intelligence (AI) systems. The genetic algorithm applies grammar operations to mutate candidate prompts, communicates the candidate prompts to generative AI systems, and selects candidate prompts that successfully evade content filters according to corresponding responses. A disambiguation model that corrects grammar in prompts is tested on the selected prompts to determine if grammar is properly corrected. Once tested, the disambiguation model is deployed in an ensemble with a classifier that outputs verdicts for prompts with grammar corrected by the disambiguation model.

Abstract: In a described example, an example no-lead semiconductor device package includes: a die pad in a central portion of a partially etched leadframe and terminals. The terminals include a device side surface formed in an upper layer of the partially etched leadframe, and a first exterior end in the upper layer; a board side surface formed in a lower layer of the partially etched leadframe extending from the upper layer; a second exterior end in the lower layer, the second exterior end inset from the first exterior end, and an inset portion extending from the first exterior end to the second exterior end. A semiconductor die is mounted to the die pad. Mold compound covers the semiconductor die, the second exterior end of the terminals and the inset portion of the terminals are exposed from the mold compound, with the mold compound extending along sides of the terminals.