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The Machine With Many Faces: Post-Exploitation Identity Misuse in SPIFFE/SPIRE
Unit 42 demonstrates that root access on a Kubernetes node lets attackers spoof SPIFFE/SPIRE attestation and harvest co-located workloads' SVIDs.
Palo Alto Networks Unit 42 describes post-exploitation techniques in which an attacker with root on a compromised Kubernetes node spoofs Linux cgroup metadata used by the SPIRE agent during workload attestation, tricking it into issuing a co-located workload's SPIFFE Verifiable Identity Document to an attacker-controlled process. The research shows the core trust assumption of machine-identity systems—that the node is trusted—collapses once root is obtained, exposing all cryptographic identities scoped to that node. Unit 42 released an open-source tool, Spooffe, for defenders to test identity exposure, and notes the technique has not been observed exploited in the wild.
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Mapping out your unknown: A threat hunter’s guide to GitHub
Datadog Security Labs publishes a threat-hunting guide with audit-log queries to detect GitHub token theft, device code phishing, and source code exfiltration.
Datadog's threat-hunting guide covers GitHub audit log queries for detecting compromised accounts, stolen personal access tokens, and malicious OAuth app authorizations. Attackers typically obtain credentials through phishing, credential stuffing, leaked secrets, or device code phishing, then map private repositories, exfiltrate source code, and pivot into connected cloud and CI/CD environments. The guide maps detections to MITRE techniques like T1078 and T1528 and documents GitHub logging quirks affecting attribution, token metadata, and visibility fields.
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6 Months on Alert: Get H1 2026 Cyber Risk Report for SOCs and MSSPs
ANY.RUN's H1 2026 report details 15 threat trends including 437% growth in fake CAPTCHA phishing and 90.7% rise in Adobe infrastructure abuse.
The report draws on interactive sandbox submissions from over 700,000 analysts and 16,000 SOC teams between January and June 2026. Attacks abusing Adobe infrastructure grew 90.7% versus H2 2025 while RMM-related attacks rose 26.5%, and custom fake CAPTCHA phishing grew 437% from Q1 to Q2 2026. ANY.RUN argues static IOCs are losing effectiveness as dead drop resolvers hide the final C2 until execution.
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InceptionRAG: Stealthy Poisoning Attack Against Retrieval-Augmented Generation
InceptionRAG fragments malicious payloads into dormant passages that trigger LLMs to self-deduce misinformation via multi-hop reasoning, bypassing existing RAG poisoning defenses.
Researchers introduce InceptionRAG, a stealthy corpus poisoning attack against retrieval-augmented generation that splits a malicious payload into a chain of individually harmless dormant passages. When retrieved together, the passages induce LLMs to self-deduce target misinformation through multi-hop reasoning, achieving over 80% attack success rate across three datasets and three LLMs under rigorous adversarial constraints. A zeroth-order suffix optimization (ZOSO) method automates authoritative suffix generation in black-box settings. The authors also propose HODOR, a document isolation defense that decouples adversarial logical dependencies.
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SpliTEE: Improving LLM Inference on Trusted Hardware with Differentially Private GPU Outsourcing
SpliTEE splits LLM inference between Intel TDX trusted execution and untrusted GPUs, using differential privacy instead of encryption to protect intermediate representations.
SpliTEE extends split inference to LLMs, running inference partly inside an Intel TDX TEE while masking intermediate inputs sent to untrusted GPUs with differential privacy rather than encryption. The authors show a prompt-reconstruction attack recovers nearly 80% of prompts from unmasked intermediate representations, motivating the masking. A global sensitivity analysis bounds the required DP noise scale, avoiding quantization and keeping models in floating point. The implementation is nearly twice as fast as full CPU-based TDX inference and 5-15 seconds faster than encryption-based Slalom with higher accuracy, evaluated on Llama-3.2-3B and Qwen3-4B.
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Credential Theft: How Attackers Steal & Use Stolen Credentials
Huntress explains how attackers steal credentials through phishing, AitM, infostealers, and dumping, then use them for lateral movement, BEC, and ransomware.
Huntress published an educational overview of credential theft, citing that roughly 70% of confirmed data breaches begin with stolen credentials. It details acquisition methods including phishing, adversary-in-the-middle attacks that capture MFA session tokens, infostealers (nearly a quarter of threats Huntress observed in 2025), Mimikatz-based credential dumping, credential stuffing, and password spraying. The piece then covers post-theft actions such as lateral movement, privilege escalation, account takeover, business email compromise, and ransomware, and closes with behavioral detection guidance and layered prevention strategies.
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NHIs Now the Number One Corporate Entry Point for Hackers
SpyCloud survey finds non-human identities like AI agents and API keys were the primary entry point in 31% of intrusions, nearly double phishing.
SpyCloud's Identity Threat Report, based on a survey of 750 security leaders at organizations with 500+ employees, found non-human identities caused 31% of intrusions versus 17% for social engineering. Only 36% of organizations actually monitor NHIs although 95% believe they have adequate visibility into them. Some 68% of respondents suffered an identity-based event, with NHI-related misuse at 42%, and organizations able to see stolen session cookies reported identity incidents at a lower rate (37% vs 50%).
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SpyCloud 2026 Identity Threat Report Finds Non-Human Identities Are Now the Leading Path into the Enterprise
SpyCloud survey of 750 security leaders finds compromised non-human identities are the top enterprise entry point, yet only 36% monitor them.
The 2026 Identity Threat Report surveyed 750 cybersecurity leaders at organizations with 500+ employees across North America and Europe. Compromised non-human identities (31%) were the most cited primary attacker entry point, nearly double phishing (17%), while only 36% of organizations monitor AI agents, service accounts and API keys. 68% of respondents reported identity-based events, averaging eight each, and 91% use AI tools but only 56% have formal governance over their privileges.
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Staying Ahead of Adversarial AI Through Agentic Source Code Review
Google Threat Intelligence details an agentic AI pipeline with human expert oversight to review source code and outpace AI-enabled attackers.
Google Threat Intelligence researchers argue that adversaries' misuse of AI raises the risk of data theft and extortion when proprietary source code is exposed. They describe a structured agentic source code review pipeline that combines AI models with skeptical validation steps and injected human domain expertise. The team reports a leap in efficacy in finding vulnerabilities before adversaries can exploit them.
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TLD Tracker: Exploring Newly Released Top
Unit 42 tracked 19 newly released top-level domains and found large-scale phishing, unwanted program distribution, and cybersquatting tied to TLD launch dates.
Researchers analyzed 19 new generic TLDs, including .zip, .bot, .ing, and .meme, released or approaching general availability over roughly 18 months. Data from passive DNS, registry zone files, newly registered domain feeds, and the Tranco top-1M list showed phishing campaigns, potentially unwanted program distribution, and domain squatting on these TLDs. Abuse correlated with each TLD's rollout phases, indicating attackers monitor general availability dates to register and weaponize domains. The IANA root database now lists over 1,000 generic TLDs.
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Attack Paths Into VMs in the Cloud
Unit 42 maps attack paths into AWS, Azure, and GCP VMs through intended features like startup scripts and SSH key pushes.
Palo Alto Unit 42 reviewed attack vectors against virtual machine services on AWS, Azure, and GCP, finding that 11% of internet-exposed cloud hosts carry Critical or High severity vulnerabilities. The attack paths rely on legitimate features such as EC2 User Data, VM custom data, EC2 Instance Connect, SSM Run Command, and serial consoles rather than vulnerabilities, and exploiting them requires attackers to first obtain control plane permissions. A compromised VM exposes not only its data but the workload identity and cloud permissions assigned to it, making identity compromise potentially more damaging than data theft. The firm places mitigation responsibility on cloud users and administrators.
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