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Search: “autonomous systems”

16 stories in the last 30d

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DomainAllCyberAIWindow24h3d7d30dAllIn the wildImportant onlyShow duplicates
CategoryAllAI safety & security 76Threat actor 31Industry 31AI industry 23AI research 21Research 16Tools 11AI policy 8AI tools & infra 7Model release 7Data breach 6Exploit / PoC 6Advisory 5Malware 4
SeverityAllcritical 2high 44medium 43low 1info 168

Evaluating Practical Enumeration and Blocking Attacks on the Snowflake Circumvention System

Ethical measurements enumerated 21,000+ Snowflake proxy IPs across ~1,000 ASes; blocking top 1% of ASes disrupts 30% of Snowflakes.

Researchers tested Snowflake's assumptions that proxy IPs cannot be easily enumerated and that blocking them causes unacceptable collateral damage. Over 48 days of real-world measurements (May-June 2025), malicious-client-style enumeration collected over 21,000 unique proxy IPs across almost 1,000 autonomous systems. Blocking the top 1% of ASes blocks more than 30% of observed Snowflakes while affecting about 2.5% of Tranco Top 1M domains, and the broker's load-aware matching leaks stable high-capacity proxies to attackers. Some proposed mitigations have already been integrated into Snowflake.

arXiv cs.CR · 6d agoResearch1

Illusion of Depth: Revealing Hidden Stereo Vision Vulnerabilities in Depth Estimation

Simple repeating patterns let attackers shift stereo-camera depth estimates by up to 20 meters, triggering emergency braking in autonomous driving frameworks at 40 km/h.

The paper reveals an intrinsic vulnerability in stereo cameras stemming from pixel sampling and calibration processes, letting attackers finely control estimated depth of real obstacles using simple repeating patterns without adversarial ML techniques. The attack was evaluated against BM and SGBM stereo matching algorithms, deep learning models PSMNet, MoCha-Stereo, and UniMatch, the stereo-LiDAR fusion model SGM-DDC, and commercial cameras ZED2 and Intel RealSense D435; on ZED2, obstacles can be displaced up to 20 meters farther or 12 meters closer. A 0.5-second attack triggered emergency braking in a popular autonomous driving framework, with feasibility confirmed at driving speeds up to 40 km/h using CARLA. State-of-the-art defenses proved ineffective, and the authors propose a similarity-score strategy to dynamically detect and suppress depth discrepancies.

arXiv cs.CR · 2d agoResearch

RobResilience: Implementing and Evaluating a Resilience Framework for Cyber-Physical Embodied Systems

RobResilience implements a runtime resilience framework for robots in Webots/ROS2, evaluating tolerable disruption, degradation, and mitigation feasibility across eight attack scenarios.

The paper implements a formal resilience framework for embodied cyber-physical systems using a PR2 robot and ROS2 in a Webots simulation. At runtime it evaluates three predicates — tolerable disruption (δ), tolerable degradation (γ), and mitigation feasibility (μ) — over a compromised device set derived from IDS confidence scores, triggering mitigation strategies when resilience is lost. Eight attack scenarios systematically covering the full predicate state space confirm runtime behavior matches theoretical definitions. The work addresses 'graceful failure paralysis,' where autonomous systems cannot distinguish safe degraded states from catastrophic hazards during attacks.

arXiv cs.CR · 1d agoResearch

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

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.

Okta Security · 9d agoResearch

Towards Tackling Application Logic Flaws through Autonomous Formal-Logic Modeling and Automated Reasoning

LL-Verifier combines LLMs with logic model checking to automatically discover logic flaws, uncovering vulnerabilities in 27 IoT access-control protocols.

Researchers present LL-Verifier, a framework that uses LLMs to autonomously convert natural-language protocol descriptions and security goals into formal logic models in a new logic language built on Maude, then applies logic model checking for exhaustive verification. The framework targets application-logic flaws that are tied to business semantics and hard to scale with manual analysis. Evaluation on 27 access-control protocols of widely used IoT devices uncovered a range of sophisticated logic vulnerabilities with security and privacy implications.

arXiv cs.CR · 7d 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

$536 and 8 Hours: AI Learns to Attack a Different PLC

Forescout used Claude to port a WAGO PLC exploit for $536 in 8 hours, and an AI-generated payload later permanently bricked the test device.

Forescout researchers used Claude Code with Ghidra, terminal access, and physical hardware to port CVE-2021-31886, a pre-authentication buffer overflow in the Nucleus FTP server, from a WAGO 750-852 PLC to the WAGO 750-831 without source code or debugger access. The final RCE development stage cost $535.74 in API fees over an 8-hour, 32-minute session (2.6k input and 1.3M output tokens) and required heavy researcher guidance. Claude produced working ICMP and UDP beacon exploits in 12 minutes after identifying that normal FTP command completion was overwriting its shellcode, fixed by omitting the CRLF terminator. A follow-up session extending the payloads toward a C2 implant wrote to flash-mapped memory and permanently bricked the PLC, underscoring the risks of autonomous agents operating against physical infrastructure.

Security Affairs · 14d agoResearchCVE-2021-318861

A Cyber Range Evaluation of Autonomous Network Incident Response Agents

Cyber range evaluation shows reinforcement learning incident response agents defend emulated networks more efficiently than heuristic policies, depending heavily on adversary behavior.

The paper evaluates agents for automated network intrusion response in a cyber range designed for human operator training, featuring variable topology, red-team emulation, and simulated users. Alerts are generated by a SIEM platform and mapped to a data modeling language used by the agents, with reinforcement learning policies optimized to minimize combined defense and availability costs using a cyber attack simulator. Reinforcement learning agents defended the system more efficiently than heuristic policies, with performance highly dependent on the adversary policy and simulated user behavior.

arXiv cs.CR · 2d agoResearch

When Agents Look Like Beacons: NIDS Evasion by Model Context Protocol Traffic

Research shows Model Context Protocol agent traffic structurally resembles C2 beaconing and evades Suricata signatures and RITA behavioral scoring in testbeds.

An arXiv study demonstrates that Model Context Protocol (MCP) JSON-RPC traffic over Streamable HTTP mimics the polling patterns of C2 frameworks like Cobalt Strike and is not flagged as anomalous by standard enterprise defenses. In a Docker testbed with eleven traffic profiles across three TLS conditions, Suricata with the Emerging Threats Open ruleset produced near-zero alerts and RITA assigned a consistent 0.0 beacon score, regardless of jitter or TLS inspection. The authors propose an agent-native network indication standard using Agent-Native ALPN and out-of-band headers.

arXiv cs.CR · 18h agoResearch1

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.

Help Net Security · 6d agoResearch1

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

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.

Help Net Security · 3d agoResearchCVE-2017-74942· 1 read

What Zero-Day Response Should Be in the Post-Mythos Era

Picus Security outlines a zero-day response playbook where defenders simulate exploit technique chains before public PoCs exist.

The article uses PaperCut NG/MF's August incident — exploitation in the wild before any patch, with the first emergency fix bypassed the same day and a third landing September 1 — as the template for AI-accelerated vulnerability response. It walks through a hypothetical CVE-2026-1001 (explicitly made up) to argue defenders should map CVEs to ATT&CK technique chains and simulate them against NGFW, WAF, EDR, endpoint hardening, and SIEM controls within minutes of disclosure. It notes disclosure-to-exploitation time has fallen from 21.5 days to hours.

BleepingComputer · 1d agoResearch in the wildCVE-2026-1001

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

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