Mitre ATT&CK
PulseAugur coverage of Mitre ATT&CK — every cluster mentioning Mitre ATT&CK across labs, papers, and developer communities, ranked by signal.
7 day(s) with sentiment data
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Anthropic report details AI misuse shift to agentic attacks
Anthropic has released a report detailing how malicious actors misuse AI models, particularly focusing on the shift from simple malware writing to more sophisticated agentic actions like lateral movement within networks…
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GDM releases AI Control Roadmap with cybersecurity-inspired threat modeling
The GDM AI Control Roadmap (v0.1) has been released, outlining a plan for internal guardrails to detect and mitigate adversarial AI agent behavior. The roadmap draws inspiration from cybersecurity frameworks like MITRE …
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New Agentra framework enhances enterprise intrusion response with multi-agent planning
A new research paper introduces Agentra, a multi-agent framework designed to automate and improve enterprise intrusion response. Agentra converts security alerts into structured incident response plans, leveraging frame…
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New dataset combines system, network, and browser logs for cybersecurity
Researchers have developed a new multi-source cybersecurity dataset by combining system, network, and browser logs from Windows endpoints. This dataset, containing 870 sessions and approximately 2.3 million events, is l…
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Open-source LLMs fall short on complex cyber threat intelligence classification
A new research paper evaluates the performance of seven open-source large language models (LLMs) on classifying complex cyber threat intelligence (CTI) reports. The study constructed a dataset of 2,076 human-annotated s…
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Qwen3.6 and Llama3.1 Show Stark Differences in Resisting Malicious Prompts
A comparative security test of local Large Language Models (LLMs) revealed significant differences in their ability to resist malicious prompts. Qwen3.6-7B demonstrated a higher susceptibility, outputting usable attack …
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AI-native closed-loop security proposed for 6G cyber-physical systems
A new survey paper proposes an AI-native, closed-loop security framework for 6G-enabled cyber-physical systems (CPSs). The proposed system aims to detect and mitigate threats at the network edge with millisecond-level p…
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Researchers automate security rule generation from attack simulations
Researchers have developed a method to automatically generate security detection rules from attack simulations. This system deterministically maps findings from Breach-and-Attack-Simulation (BAS) tools to starter Sigma …
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Anthropic report: AI now orchestrates complex cyberattacks
Anthropic's recent report details how AI is increasingly used in cyberattacks beyond simple phishing. The analysis, which mapped 832 banned accounts to the MITRE ATT&CK framework, indicates a shift from AI assisting wit…
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AI agents pose new worm threat; local LLM memory and cyber threat map released
A new paper details AI agents capable of adapting to security measures, potentially evolving into more effective computer worms, highlighting the need for enhanced AI system defenses. Separately, a Rust library called M…
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New TTPrint Method Enhances Cyber Threat Intelligence Analysis
Researchers have developed TTPrint, a novel method for extracting MITRE ATT&CK techniques from cyber threat intelligence reports. This system employs a "diverge-then-converge" approach, first broadly identifying candida…
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CyberGraph RAG uses TigerGraph to improve LLM cybersecurity analysis
Researchers developed CyberGraph RAG, a system designed to improve how large language models handle cybersecurity data by leveraging graph databases. Unlike traditional RAG which struggles with the relational nature of …
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Spartans-GraphRAG uses knowledge graphs to cut LLM token costs
A new system called Spartans-GraphRAG has been developed to make Large Language Model (LLM) inference more efficient, particularly for complex tasks like cybersecurity threat intelligence. This system leverages knowledg…
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AI module Smart-SIEM boosts open-source SIEM web attack detection
Researchers have developed an AI module called Smart-SIEM to enhance the detection capabilities of open-source Security Information and Event Management (SIEM) systems. This module enriches behavioral profiling by incor…
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LLM agents vulnerable to Morse code and other encoding attacks
Security researchers demonstrated a novel prompt injection attack against Bankr, an AI financial assistant, by encoding instructions in Morse code. This method bypassed traditional content filters because the LLM interp…
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LLMs leverage code analysis for improved malware attribution
Researchers have developed LCC-LLM, a framework and dataset designed to improve malware attribution using large language models. The system leverages code-centric representations, including decompiled C code and assembl…
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Retrieval-Augmented LLMs Enhance Cybersecurity Incident Analysis Efficiency
Researchers have developed a Retrieval-Augmented Generation (RAG) system to automate the analysis of cybersecurity incidents. This system uses targeted queries and a library of MITRE ATT&CK techniques to extract indicat…
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CyberAId platform uses AI agents to bolster financial cybersecurity
A new paper proposes CyberAId, a hybrid multi-agent system designed to enhance cybersecurity for financial institutions. The system integrates specialized AI sub-agents with existing SIEM/XDR telemetry, rather than repl…
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DeepStage uses AI to learn autonomous defense against multi-stage cyberattacks
Researchers have developed DeepStage, a new framework utilizing deep reinforcement learning to create autonomous defense policies against multi-stage cyberattacks. The system models enterprise environments as partially …
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OntoLogX uses LLMs to extract actionable threat intelligence from cybersecurity logs
Researchers have developed OntoLogX, an AI agent designed to extract Cyber Threat Intelligence (CTI) from raw cybersecurity logs. The system utilizes Large Language Models (LLMs) combined with a lightweight log ontology…