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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.

Recorded Future · 22d agoResearch

$20 per zero-day is already the WordPress plugin reality

TrendAI and CHT Security used an AI pipeline to find over 300 verified WordPress plugin zero-days at roughly $20 per vulnerability.

A pipeline built in three days by TrendAI and CHT Security, presented at Ekoparty Miami, paired AI-driven static analysis with automated Docker provisioning and Chrome DevTools MCP dynamic verification to surface more than 300 critical zero-days in WordPress plugins within 72 hours. The run consumed about 222 million tokens across 95 tasks, averaging roughly $20 per verified vulnerability, with findings including pre-auth RCE, SQL injection, privilege escalation, SSRF, and an AI-assembled downgrade attack chain. Dynamic verification eliminated over 80% of false positives, but manual review at 30-60 minutes per finding remains the bottleneck, straining ZDI and NIST triage backlogs.

Help Net Security · 24d agoResearch1

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.

The Hacker News · 5d agoResearch2

The modern attack chain: Rethinking Google Workspace security in the age of AI

Analysis of Vercel and Composio breaches shows stolen OAuth tokens, not phishing email, now open Google Workspace attacks — a path authorized AI agents follow.

The author analyzes the Vercel and Composio breaches as the same OAuth-first attack chain run twice: a stolen OAuth token, obtained via a compromised supplier, becomes the entry point rather than email. These tokens survive password resets, are hard to observe, and let attackers read Gmail and Drive data, take over accounts, and pivot laterally using stored credentials and password-reset magic links. The piece warns that authorized AI agents with overbroad OAuth grants can unintentionally traverse the same path — accessing inboxes, reading sensitive content, and exfiltrating data downstream — without any malicious actor or compromised credential.

Help Net Security · 1d agoResearch in the wild

Can your coding style predict whether your code is vulnerable?

University of Massachusetts Dartmouth researchers present VulStyle, a stylometry-based vulnerability detector that also exposes benchmark reliability problems.

VulStyle combines stylometric features with syntax-tree structure and source tokens, pre-trained on about 4.9 million functions across seven programming languages and fine-tuned on five vulnerability detection datasets. It beat token-only detectors on some benchmarks but its F1 drops sharply on DiverseVul, which the authors link to noisy labels inflating reported performance across popular datasets. The authors argue style-aware detection should be harder to evade but did not test this empirically, and they note that uniform LLM-generated code may strip away the individual developer style the model depends on.

Help Net Security · 24d agoResearch1

Scaling Verification of Cryptographic Software with Aeneas, Rust, and Lean

Microsoft SymCrypt implementations of SHA-3 and ML-KEM verified in Lean via Aeneas-extracted Rust models, with AI agents writing proofs.

The paper develops a methodology for verifying production Rust cryptographic code by using Aeneas to extract pure models into Lean, avoiding low-level pointer and aliasing reasoning. Applied to Microsoft's SymCrypt, it verifies SHA-3 and ML-KEM implementations ported from C to Rust and extends SymCrypt with FrodoKEM, ML-DSA, and HPKE. A 237 KLOC Lean development establishes safety, panic-freedom, and functional correctness of 16.7 KLOC of Rust supporting post-quantum cipher suites on x86-64 and ARM. AI agents autonomously write formal proofs verified by the Lean kernel, and evaluation shows verified Rust meets SymCrypt's performance and portability requirements.

arXiv cs.CR · 2d agoResearch1

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.

Google Threat Intelligence · 29d agoResearch1

Why The Vulnerability Backlog Is About To Get Worse

Recorded Future analysis says AI-driven vulnerability discovery and faster weaponization will grow the triage backlog while shrinking defenders' response windows.

Disclosed vulnerabilities rose from roughly 21,000 in 2021 to nearly 50,000 in 2025, while Recorded Future assessed only 446 as actively exploited in 2025. VulnCheck found nearly 29% of 2025 KEV entries were exploited on or before CVE publication. The authors argue AI-assisted discovery and automated exploit development will multiply credible reports, cut disclosure-to-exploit time toward minutes, and force re-evaluation of medium-severity flaws as exploit-chain components.

Recorded Future · 22d agoResearch

Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection

Unit 42 clusters behavior of 40,000+ AWS identities from 125 cloud environments to map functional roles and enable lightweight SQL-based detection.

Palo Alto Unit 42 built an unsupervised behavioral clustering model using UMAP and HDBSCAN on AWS CloudTrail logs to map cloud identities to functional roles such as administrators, backup services, security tooling and DevOps. The study analyzed over 40,000 identities across 125 cloud environments over two months. The researchers show that heuristics extracted from the clustering map can be implemented in standard SQL, enabling role classification at scale without running a continuous ML pipeline. The methodology extends to audit logs from other cloud providers, SaaS and Kubernetes.

Palo Alto Unit 42 · 3d agoResearch

Signing TLS handshakes inside a TPM

Blog post demonstrates signing TLS handshakes in Go with a TPM-held private key; full text was unavailable behind an Anubis challenge.

The title indicates a technique write-up on using a TPM-protected private key to sign TLS handshakes from Go. The full article text was not retrievable because the site serves an Anubis Hashcash-style proof-of-work challenge to automated fetchers, so no additional technical details (code, library, or threat model) could be extracted at collection time.

Lobsters · security · 9d agoResearch

DDRop Attack Forces Intel TDX Confidential VMs Into Debug Mode and Exposes Memory

KU Leuven and ETH Zurich researchers released DDRop PoC hardware that forces Intel TDX confidential VMs into debug mode and exposes plaintext memory.

Researchers from KU Leuven, ETH Zurich, Google, and Durham University published proof-of-concept code, hardware designs, and firmware for DDRop, a DDR5 interposer that injects parity errors to drop selected cache-line writebacks. Because Intel TDX, Intel Scalable SGX, and AMD SEV-SNP lack per-line cryptographic freshness, processors decrypt and accept stale DRAM contents as current state. The PoC flips a victim's ATTRIBUTES.DEBUG flag to enable TDX debug mode, then copies victim memory in plaintext and can forge attestation reports. Affected environments include Intel 5th- and 6th-generation Xeon Scalable with TDX; Intel says the attack falls outside its cloud-computing threat model.

GBHackers · 2d agoResearch

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.

GBHackers · 7d agoResearch 2 sources