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When the Whole Company Adopts AI: What It Does to Your SOC
Analysis of 16.9 million SOC alerts finds AI-related alerts at 0.43%, growing 685% since February, with 94.1% noise and 0.02% real attacks.
A review of roughly 16.9 million SOC alerts found about 73,000 (0.43%) were AI-related, a share that grew 685% between February and June 2026. Of AI-related alerts, 94.1% were noise, 5.8% genuine risks, and 0.02% real attacks; 79.8% received benign verdicts, 81.7% were automatically suppressed, and only 5.4% reached a human analyst. The only confirmed attacks were phishing campaigns that weaponized AI brand names as lures, while developer coding agents spawning shells and reading credential stores routinely tripped detections written before AI agents existed.
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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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1Password's AI patching benchmark is misleading
Trail of Bits reanalysis says 1Password's 26% AI clean-fix rate is misleading; 86% of eligible patches blocked exploits.
Trail of Bits critiques 1Password's FLAWED AI patching benchmark, arguing its 26% clean-fix headline mixes trials where agents were instructed to apply wrong fixes (22% of data) with trials that prohibited compiling or testing (36%). Restricting to reasonable conditions, 2,634 of 3,067 patches (86%) blocked the supplied exploit. Trail of Bits also reports 12.5% of 2,265 developer first fixes failed in its own 2024-2026 assessments, and released post-patch-validation and review-walkthrough agent skills.
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Hunting Vulnerabilities Using Frontier Models
Okta used frontier AI models GPT-5.5 Cyber and Mythos via OpenAI and Anthropic programs to scan millions of code lines for vulnerabilities.
Okta describes using frontier AI models, including GPT-5.5 Cyber Preview (TAC) and Mythos Preview, through OpenAI's Daybreak Cyber Partner Program and Anthropic's Project Glasswing to hunt vulnerabilities across its product codebase. The team built a custom Python orchestrator with strong isolation, vendor-agnostic model support, and four distinct scanning pipelines executed as isolated Codex or Claude Code sessions with progressive context loading to reduce context bloat. Human experts and AI agents worked both autonomously and in paired hunts, and Okta reports the best results when humans and agents taught each other.
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Trust-Aware Adaptive Disclosure for Inference Privacy Preservation in Multi-Agent Networks
Researchers propose a trust-aware privacy framework for multi-agent systems that adapts message disclosure to reduce goal inference attacks.
The cs.CR paper addresses privacy-preserving consensus in networked multi-agent systems where observing adversaries attempt to infer each agent's hidden goal from its messages. A Trust-Aware Privacy Control framework uses a trust-dependent stochastic policy to adapt information release, trading off consensus performance and privacy. Experiments show reduced adversarial goal inference accuracy versus representative baselines while maintaining competitive consensus utility, with relevance to deployments such as healthcare management and smart grids.
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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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Automating Attack Graph Construction for Agentic Pentesting. Towards Neuro-Symbolic Vulnerability Hunting
Semi-automated pipeline converts Trivy, Semgrep, Nmap output into MulVAL attack graphs for agentic pentesting, 53.7% mean vulnerability coverage in CyBench.
The paper presents a semi-automated pipeline that parses Trivy, Semgrep, and Nmap findings into MulVAL predicates and uses an LLM-assisted process to build domain-specific Datalog rules linking scanner evidence to attack techniques. MulVAL/XSB then performs symbolic inference to generate structured, auditable attack paths for agentic pentesting. Evaluated on 54 web CTF tasks from CyBench, every task produced at least one goal-reaching graph with 53.7% mean ground-truth vulnerability coverage, 51.9% full coverage, and an 83.9% noise-path rate. Median end-to-end runtime was 24.9 seconds, making the pipeline runtime-practical for agentic workflows.
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Turn it off and on again, but for critical infrastructure
KTH researchers trained a reinforcement-learning intrusion response agent on an emulated segmented OT network that autonomously resets hosts and processes to disrupt intruders.
Researchers at KTH Royal Institute of Technology built a containerized replica of a segmented industrial network, attacked it across 14 days, and captured 40,000 30-second traffic intervals to train a defense agent under partial observability. The agent observes six packet-count numbers per interval, maintains 500 running state hypotheses, and can reset supervisory hosts, water tank processes, or entire subnets, with resets rebooting the target, renewing credentials, and changing its IP. The best agent approached a full-visibility baseline but depends on an assumed attacker behavior model; the testbed comprised three supervisory hosts, two PLCs, two tanks, weak credentials, and CVE-2017-7494 exposure. The team released its implementation and plans validation on a real industrial testbed with a partner.
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VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities
Introduces VEX-Bench, 75 expert-labeled real-world cases testing whether LLM agents can assess supply chain vulnerability exploitability; frontier models reach about 80% F1.
VEX-Bench is the first benchmark evaluating LLM agents on assessing whether upstream dependency vulnerabilities are exploitable in downstream projects, with 75 real-world expert-labeled cases across Python, Java, and Go mined from GitHub. Nine models across three agent harnesses were evaluated; GPT-5.5 and Claude Opus 4.6 reach approximately 80% F1 on binary vulnerability-status classification, but only GPT-5.5 surpasses 70% macro-F1 on fine-grained justification classification. The gap highlights the difficulty of moving beyond binary exploitability calls to explaining exploitability reasons, unlike prior benchmarks targeting zero-day settings.
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PrivEscalate: Measuring and Augmenting the Threat of LLM-Automated Linux Privilege Escalation
Researchers release PrivEscalate, a 531-scenario benchmark showing LLM agents' Linux privilege-escalation success varies by vulnerability class, plus PrivEscAgent, a domain-specialized agent that boosts success.
The paper introduces PrivEscalate, an open-source benchmark of 531 Dockerized Linux privilege-escalation scenarios spanning 14 sub-categories, plus 329 parameterized variants measuring sensitivity to environmental distractors. Evaluating six LLMs across three agent architectures shows capability is heterogeneous across vulnerability classes, sensitive to perturbation, and architecture-dependent. The authors also present PrivEscAgent, a wrapper adding deterministic enumeration, category matching, and step planning that outperforms prior privesc-agent baselines without modifying the underlying LLM. The benchmark is released to support LLM agent evaluation, defensive tool validation, and red-team training.
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Has anybody seen my keys? A key-hierarchy strategy for rack-level security
Oxide's RFD 0301 proposes a rack-level key hierarchy using Shamir secret sharing and a trust quorum to protect data-at-rest keys.
Oxide's request for discussion (RFD 0301) lays out a key-hierarchy strategy for rack-level security, deriving keys from a rack secret protected by Shamir secret sharing across a trust quorum of sleds, with keys exchanged over authenticated sprockets sessions. The document maps which keys protect control-plane data, metrics, Crucible extents, and authentication tokens, and defines open questions on key lifecycle, locality, and compromise handling. Future work includes sealing shares with the root of trust so an attacker would need to steal K whole sleds to reconstruct the rack secret.
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Cost-Aware Hierarchical Multi-Agent Ransomware Detection and Family Attribution
Researchers propose a hierarchical multi-agent system that cuts ransomware analysis cost by 44% while reaching 96.57% detection accuracy.
An arXiv paper (2609.04820) presents a Cost-Aware Hierarchical Multi-Agent System (HMAS) for adaptive ransomware detection and family attribution. Specialized agents run static analysis first, with dynamic and memory modalities invoked only when confidence is insufficient or specialists disagree; a Meta Orchestrator balances accuracy against computational cost via a cost model, and a locally deployed LLM verifies difficult cases. The system achieved 96.57% accuracy, 0.96 F1-score, and 0.99 ROC-AUC for binary detection, and 0.90 macro-F1 for multiclass family attribution. Average analysis cost dropped 43.97% versus exhaustive analysis, with 56.05% of cases resolved using static evidence alone.
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The Model Proposes, the Code Disposes: A Pre-Registered Ablation of a Verifier-and-Acceptance Stage in an LLM-Orchestrated Offensive-Security Agent
Pre-registered ablation finds a model verifier stage in an LLM offensive-security agent suppresses findings; removing it eliminated suppression with precision tradeoff.
The paper evaluates a verifier-and-acceptance stage in an LLM-orchestrated offensive-security agent via a pre-registered 20-run confirmatory ablation and a 2x2 factorial study with 40 runs on vulnerable lab targets. Removing the stage eliminated pre-report suppression (median 2 vs 0 findings, p = 0.00003) but reduced model-blinded shipped precision (0.471 vs 0.353, p = 0.0087). Suppression was attributed to the model verifier rather than deterministic acceptance rules, and an instrumented canary recorded zero external contacts in all 60 runs. The full design retained 93.8% of model-adjudicated true candidates but failed its pre-registered non-inferiority floor of 0.90.
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TrajMark: Ownership Attribution and Segment-Level Tamper Localization for Coding-Agent Trajectories
Researchers introduce TrajMark, a training-free watermarking framework for coding-agent trajectories that recovers ownership, detects 95.5-100% of edits, and localizes tampered regions.
TrajMark is a training-free, symmetric-key, visible-only watermarking framework for coding-agent trajectories that separates robust ownership attribution from fragile local integrity verification. A sparse owner layer encodes a six-bit deployment identifier by rewriting keyed READ actions into masked linear equations, while a localization layer inserts linked Q12 seals that commit to protected critical-action segments. Across three coding-agent frameworks and three LLMs, it recovers the exact owner in all clean full-watermark batches, detects 95.5%-100% of single-site edits, and localizes 95.8% of random corruptions to an accepted protocol region. Owner marking adds no trajectory actions and matched Pass@1 is 26.9% versus 26.3% for unwatermarked runs.
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ResidualAuth: What Authorization State Must Language Agents Preserve under Revocable Delegation?
Formalizes residual authorization state language agents must preserve under revocable delegation; token-budget summaries mostly fail while hard gates stop unauthorized effects.
The paper shows two authorization histories with identical current permissions can require opposite decisions after the same direct-edge revocation, formalizing the needed information as residual authorization state. Exponentially many future-distinct states can share one transitive closure, with exact or tight asymptotic bounds on the state an exact monitor requires. Across four open-weight models, fixed 256-token summaries solved at most 2 of 16 paired episodes while authenticated current-query reads solved 15-16 of 16. A hard effect gate reduced eight observed unauthorized effects to zero without changing preceding attempts.
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BlueSTAR: Tiered Agentic Architecture for Autonomous Cyber Defense
BlueSTAR is a tiered agentic LLM architecture for autonomous cyber defense, validated on live enterprise IT/OT cyber ranges against seven attack chains.
Researchers present BlueSTAR, a tiered agentic architecture for autonomous cyber defense in enterprise IT/OT networks that transforms high-volume security telemetry into compact indicators of compromise. It pairs deterministic containment for known threats with LLM reasoning for attacks requiring contextual and cross-cycle analysis, and introduces a resilience metric jointly weighing attacker reach, mission-critical impact, and defensive disruption. Evaluation on two live cyber ranges with seven attack chains based on real-world intrusion techniques covered credential theft, repeated compromise, concurrent attackers, and attacks on physical processes.
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LLM-Based Penetration Testing in the Presence of Honeypots
Studies honeypot-aware budget allocation for LLM attack agents, showing detector-guided policies let agents skip deception and compromise real hosts efficiently.
The paper formalizes LLM attacker behavior against honeypots as a budgeted decision process, where agents choose to continue or skip targets when honeypot suspicion arises. A detector-guided policy lets LLM agents allocate execution budget effectively across a mixed host pool in a controlled testbed. Findings show LLM-driven attackers can reason about heterogeneous artifacts and use honeypot suspicion to guide target selection, challenging traditional deception defenses that rely on realism and obscurity against human or script-driven attackers.
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The State of AI-Enabled Malware August 2026: From Brand Abuse to Agentic Execution
Unit 42's August 2026 report tracks the rise of AI-enabled malware, from brand abuse to agentic execution, and how behavioral detection stops AI-authored code.
Palo Alto Networks Unit 42 released its August 2026 assessment of AI-enabled malware, covering attacker use cases from brand abuse to agentic execution. The report details how existing behavioral detection and endpoint analytics can stop AI-authored code before execution.
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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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Identifying Agentic Automation with Behavioral Telemetry: Part 2
Akamai details behavioral telemetry signals for identifying agentic automation traffic in the second part of its research series.
Akamai published part two of its series on identifying agentic automation using behavioral telemetry. The post appears on Akamai's security research blog and focuses on detecting AI-agent-driven traffic. Full article text was unavailable at classification time, so classification relies on the title and source.
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The AI Malware Maturity Gap
Recorded Future introduces AIM3, a five-level maturity model for AI malware, showing current attacker AI use is mostly AI-assisted rather than autonomous.
Recorded Future proposes AIM3, a five-level model defining AI malware from LLM-translated to LLM-embedded, spanning experimentation to fully autonomous agentic campaigns. Public examples remain early-stage: PROMPTFLUX uses Google Gemini to rewrite its VBScript dropper (Level 1), while Lamehug/PROMPTSTEAL, attributed to APT28, invokes the HuggingFace API to generate reconnaissance commands (Level 3). The authors argue most current AI malware augments existing tradecraft rather than enabling one-click autonomous attacks.
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terms.txt: A Consent and Compensation Protocol for Agentic Web Access
terms.txt specifies a robots.txt-style protocol for per-path, per-purpose AI crawler consent and compensation, with enforcement adding 0.20-0.65 ms per request.
The paper documents that automated clients now make up most web requests, that training dominates Cloudflare-classified crawling, and that the largest AI platforms fetch thousands of pages per returned visitor while robots.txt cannot express identity, purpose, terms, or price. It specifies terms.txt plus an origin-enforced exchange using Web Bot Auth signatures, signed intent, delegation tokens, HTTP 402 negotiation, and signed receipts. A dependency-free implementation adds 0.20 to 0.65 ms per request on one vCPU.
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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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How to correlate Kubernetes audit logs with container runtime data
Elastic Security Labs shows how to join Kubernetes audit logs with Defend for Containers runtime data to investigate service account abuse and container escapes.
Elastic Security Labs demonstrates correlating Kubernetes audit logs with Defend for Containers (D4C) runtime telemetry in Elastic. In an Amazon EKS lab, a compromised workload service account performed discovery, read secrets, minted a token, created a privileged pod, and execed into it to attempt a container escape via nsenter and chroot. The escape wrappers appeared only in the decoded Kubernetes audit requestURI, not in runtime process events. The post covers join fields, prebuilt EQL sequence rules, and continues the control-plane correlation thread from the TeamPCP container attack scenario and the Hugging Face intrusion write-up.
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The best human hacking team still out-solved the best AI team
Hack The Box 2026 benchmark data shows AI agents helped top teams but human-only teams still solved everything while best AI teams stalled at 32 of 36 challenges.
At the 2026 Global Cyber Skills Benchmark (Project Nightfall) run by Hack The Box, 93 designated AI agent accounts across 54 teams held 2.7% of registered accounts but produced 4.2% of submitted flags and 4.6% of awarded points, and appeared in 17 of the Top 25 finishers. Median solve time dropped from 26 hours in 2024 to 13.8 hours in 2026, though the data cannot attribute the change to AI. At the November 2025 NeuroGrid CTF, AI-augmented teams solved challenges 3.2x faster overall but only 1.69x among the Top 5%, and the only team to complete all 36 challenges was human, while the best AI team stopped at 32.
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AI is changing what Salesforce security needs to govern
WithSecure's Trust Mapping paper proposes a framework for governing trust relationships across Salesforce workflows, AI agents, integrations and connected SaaS systems.
WithSecure's paper 'Navigating Trust in the Modern Salesforce Ecosystem' introduces a Trust Mapping Framework spanning five domains: entities, information, connections, actions and system outcomes. It applies to Salesforce, Agentforce, Headless 360, third-party SaaS and AI-assisted workflows where users, AI agents, APIs and integrations form trust relationships. A Discovery step maps relationships, a Governance step assesses, restricts or retires them, and the paper defines 'trust drift' such as stale credentials, excessive access and unvalidated AI recommendations.
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