Threats Tagged 'apt'

For security researchers studying unconventional side-channel attacks, acoustic keylogging is something of a Hello World: a foundational problem that’s been tackled many times. A recent paper authored by researchers across three Japanese universities cites six previous studies on the topic that date as far back as 2004. While earlier experiments showed theoretical promise, they came with real-world caveats so severe that it made them all but impractical for actual espionage. The authors of this latest study, however, claim to have overcome most of those limitations. Today, we look at how they pulled it off, and assess whether their method holds up in real-world scenarios. What makes this new approach different? Previous acoustic keylogging techniques were fundamentally flawed. Best-case scenarios required prior training on the target’s specific keyboard model. Worst-case scenarios required a complex microphone array to isolate the subtle acoustic differences between keystrokes. Crucially, almost all prior models failed outside silent environments, which rendered the attack vector virtually useless. The Japanese research team demonstrated reliable keystroke interception even if the target was sitting nearby in a public space, sound was being recorded in an online meeting, or the researchers were using a contact microphone to eavesdrop through a wall. All this with strong model accuracy and a minimal training dataset. Their process needs a sample of just 150 to 200 keystrokes to reach a 99% accuracy rate for subsequent typing. Attack scenarios and core methodology proposed by the Japanese researchers. Source How to crack 200 keystrokes in under 50 iterations To understand how the researchers achieved such high accuracy and adaptability, we have to look at their audio processing pipeline. Their analysis begins by automatically segmenting a raw recording into discrete keystrokes. This data is then passed through a specialized algorithm that simplifies the subsequent audio analysis. Next, the system clusters together acoustically similar signals. The assumption is that the members of one cluster map to the exact same key. One particularly intriguing takeaway was isolating the spacebar sound from all the rest. Because the spacebar produces a distinctly unique sound profile compared to other keys, identifying it provides reliable word boundaries. This streamlines the next phase: feeding the preprocessed acoustic data into specialized language models for inference. Yes, the method relies on not one but two language models. The first model performs multiple passes over the audio stream to map acoustic signatures to potential keyboard characters. During each pass, the model leverages dictionaries to hypothesize character mapping, and check whether the resulting text aligns with standard words. The second model handles the final refinement pass: it ingests thoroughly pre-processed data rather than raw inputs. The method doesn’t stop there: unrecognized keystrokes undergo manual analysis, with analysts injecting educated guesses before re-running the recognition pipeline once again. The goal of looping through these multiple iterations is to achieve complete recognition across the keyboard from an ultra-compact dataset of ideally no more than 200 captured keystrokes. This marks a major shift from legacy methods, which relied on massive training datasets. Clustering the sounds of keystrokes permits grouping similar acoustic profiles together prior to recognition. Notice how distinctly the spacebar sounds stand out: they make subsequent text reconstruction vastly simpler. Source Research results To validate their theoretical model, the researchers created an experimental testing setup: Clustering the sounds of keystrokes permits grouping similar acoustic profiles together prior to recognition. Notice how distinctly the spacebar sounds stand out: they make subsequent text reconstruction vastly simpler. Source The team tested four distinct la…

Microsoft has linked a global campaign targeting hospitality Wi-Fi networks to the Russian threat actor Midnight Blizzard, also known as APT29. [...]

This report highlights a cyber espionage campaign attributed to the Iranian APT group Charming Kitten, targeting eight countries and eight critical sectors simultaneously. The campaign, dubbed Operation Olalampo, affects Egypt, Saudi Arabia, UAE, Turkey, Hungary, Turkmenistan, Israel, and South America, focusing on government, healthcare, financial services, energy, education, telecommunications, defense, and industrial sectors. The information is sourced from a Reddit post linking to a GitHub repository simulating adversary tactics. No specific vulnerabilities or exploits are detailed, and no affected software versions are identified.

CriticalCampaignEgyptSaudi ArabiaUnited Arab Emirates+4#blueteamsec+asknetsec+information_security#reddit#high-priority

BlueShell is an open-source remote access trojan developed in Go language, primarily used by Chinese-based threat actors. A variant of BlueShell has been identified in post-intrusion activities by APT groups including BlackTech, targeting organizations in Japan, South Korea, and Thailand. This variant differs from the original through a dedicated dropper mechanism, proxy server-based C2 communication, and anti-forensic capabilities. The dropper deploys the variant to /tmp/kthread, disguises it as a Linux kernel worker process, and removes filesystem traces. Recent variants observed since 2024 include XOR-encoded configuration data and proxy functionality, indicating continuous development. The malware performs hostname verification, validates C2 certificates, and implements commands for file transfer, remote shell, and SOCKS5 proxy capabilities.

MediumMalwareIndonesiaJapanSingapore+2#anti-forensic#apt#proxy communication

SharePoint July 2026 deserialization RCE: lab PoC and captured artifacts for detection Source: https://sp-poc.wismansec.com/

AhnLab monitored Advanced Persistent Threat attacks targeting South Korea during June 2026, identifying multiple attack types distributed primarily through spear phishing campaigns. Threat actors disguised malicious files as work-related documents, with LNK files being the most common delivery method. Six distinct attack types were observed, employing various techniques including malicious PowerShell commands, AutoIt malware, curl.exe abuse, GitHub repository exploitation, Task Scheduler persistence, DLL side-loading, and Python backdoors. These attacks deployed Infostealers, keyloggers, backdoors, and remote access tools like XenoRAT. Once executed, the malware established persistence, exfiltrated system information, and enabled remote control of compromised systems. Organizations are advised to verify email senders, avoid opening files from unknown sources, apply security patches, and maintain updated antivirus software to mitigate these persistent threats.

MediumMalwareSouth Korea#xenorat#autoit#task scheduler

For years, the cyber security industry tracked AI as a force multiplier: something that made existing attack techniques faster, cheaper, and more accessible. That framing was accurate. But the Annual AI Security Report 2026 from Check Point Research documents a transition that goes further. AI has crossed from assistant to operator. Where it once helped attackers prepare, it now runs the operation. Key observed findings AI has crossed from development aid to live attack operator. It now does the hands-on work inside live intrusions, from China-nexus espionage campaigns to a criminal breach of multiple Mexican government agencies and has spread from nation states to ordinary cyber criminals. AI now builds deployment-ready malware and attack suites. Its involvement is often invisible in the finished artifact: one developer used an AI environment to produce VoidLink, an 88,000-line command-and-control offensive framework, in under a week. Attackers prefer commercial models, and now abuse them by exploiting the agentic architecture, not just single prompts. Most actors favor jailbroken mainstream models over self-hosted ones, and the durable bypass is now a planted configuration file an agent loads and trusts across sessions. An AI-enabled criminal tooling market has matured. Phishing-as-a-service kits now embed a language model with the jailbreak built in, and conversational AI voice-agent services run vishing and one-time-passcode theft at scale. Virtual Identity is no longer a reliable trust anchor. Voice, face, documents, and live video are now cheap to forge convincingly and are widely used in attacks taking multi-channel social engineering to a new level of integration. AI itself is an expanding attack surface. Models cannot always separate data from instructions and content they process might influence the model’s behavior; the surrounding stack adds ordinary software vulnerabilities and supply-chain risk, all in a rapidly evolving ecosystem where security practices not always mature. Indirect prompt injection is on the rise. Detections of longer malicious payloads increased sharply, rising roughly fivefold between March and May 2026 and approaching 1% of observed prompts in May. Longer payloads are more typical of content-borne and agentic attack paths, this pattern suggests that indirect prompt injection is becoming more operationally relevant. Enterprise data leakage through GenAI is persistent and growing risk . High-risk prompts doubled from 2% to 4% during the last year, while organizations used an average of 10 AI applications each month, many without official approval. Data exposure risks are not evenly distributed across the verticals . Sector-level analysis reveals that AI-related data exposure risks are not evenly distributed across the verticals, and correlate both with AI usage patterns and security maturity. Business Services recorded the highest rate of high-risk GenAI prompts at 5.91%, meaning nearly one in every 17 AI interactions carried a significant risk of sensitive data exposure. To read the full findings, access the AI Security Report 2026 from Check Point Research here. The post AI Security Report 2026 appeared first on Check Point Research .