Search: “access-control”

6 stories in the last 7d

Researchers verify a class of EverParse validators that correctly enforce access-control policies, deploying a machine-checked enforcement gate on seL4.

The paper targets enforcement-code bugs in commercial security gateways by proving that forward-only, backtrack-free EverParse validators are verified recognizers for a bounded finite-state class that includes access-control decision functions with fixed-offset fields and bounded disjunction. Encoding a bounded policy language into a fixed-size byte buffer allows an SMT solver to verify the enforcement code once, covering all byte values, policies, requests, and sessions. Editing rule content over a fixed endpoint set requires no new proof, while adding endpoints reruns the toolchain. A deployment on the seL4 microkernel ensures every request passes through the gate and unverified components cannot corrupt the enforcement chain.

arXiv cs.CRSep 11, 06:33 UTC · 4d 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.CRSep 9, 17:58 UTC · 6d agoResearch1

Fortinet FortiSandbox Vulnerability Allows Unauthenticated Attackers to Access Sensitive Information

Fortinet fixed CVE-2026-26084, an unauthenticated access-control flaw in FortiSandbox GUI rated 8.9 CVSS, with no known exploitation yet.

Fortinet disclosed CVE-2026-26084 (advisory FG-IR-26-166), a CWE-284 improper access control flaw in the GUI of FortiSandbox, FortiSandbox Cloud and FortiSandbox PaaS, rated 8.9 CVSS v3.1. An unauthenticated remote attacker can send specially crafted HTTP requests to control NAT rules and expose sensitive information. Affected versions include FortiSandbox 4.4.0-4.4.8 and 5.0.0-5.0.5 (plus Cloud/PaaS 5.0.4-5.0.5), fixed in 4.4.9 and 5.0.6. Fortinet researcher Adham El Karn found the flaw internally and the September 8 advisory reports no known exploitation.

GBHackersSep 9, 07:15 UTC · 6d agoVulnerability 2 sources CVE-2026-26084

Mass Scanning Targets Exposed Vite Servers to Steal AWS Keys and Azure Tokens

Attackers mass-scanned exposed Vite dev servers exploiting CVE-2026-39364 to read .env files and steal AWS keys and Azure tokens, per F5 Labs honeynet data.

Honeynet telemetry recorded 807 session-grouped attacks and roughly 32,000 raw events in August 2026 against exposed Vite development servers. CVE-2026-39364 (CVSS 7.5) is an unauthenticated file-read flaw in Vite 7.1.0 through 7.3.2 and 8.0.5 that bypasses server.fs.deny via query parameters like ?raw and ?import&raw to return files in plaintext. Attackers also probed older Vite flaws CVE-2025-30208, CVE-2025-31125, and CVE-2024-45811, and used wordlists to locate .env files, AWS credential files, Azure token stores, and Terraform state across Linux, container, and web paths. Exposed secrets could enable lateral movement or full cloud account takeover.

GBHackersupdated 11:12 UTC · 11h agofirst 05:35 UTC · 17h agoExploit / PoC in the wild 4 sources CVE-2026-39364 CVE-2025-30208 CVE-2025-31125+1 CVEs

AI workflows may be creating a dangerous new authorization blind spot

Noma Labs researchers describe 'workflow identity hijacking,' letting unauthenticated users trigger privileged AI workflows that execute actions with high-privilege service accounts.

Noma Labs lead researcher Sasi Levi detailed 'workflow identity hijacking,' where benign unauthenticated inputs via support inboxes, GitHub issues, or web forms trigger enterprise AI pipelines that execute privileged actions. The workflow runs using high-privilege service accounts or developer API keys, decoupled from the requester's identity, effectively creating a confused-deputy condition. Unlike prompt injection, the model behaves correctly; the failure lies in authorization enforcement at the workflow layer, and activity blends into routine automation. Mitigations include identity-aware access at execution points and user-context propagation between AI outputs and downstream operations.

CSO Onlineupdated Sep 10, 12:57 UTC · 5d agofirst Sep 10, 12:04 UTC · 5d agoAI safety & security 2 sources

When the prompt becomes the payload: A practical pen-testing guide for GenAI, LLM and RAG applications

CSO Online publishes a practical penetration-testing guide for GenAI, LLM, and RAG applications, covering prompt injection, retrieval poisoning, and tenant isolation testing.

The guide frames LLM applications as attack graphs spanning prompts, retrieval layers, vector stores, tools, identities, and downstream APIs, arguing that conventional web testing misses instruction-vs-data channel risks. It builds on OWASP prompt injection guidance (direct vs. indirect injection) and NIST's 2025 adversarial machine-learning taxonomy, noting that RAG and fine-tuning do not remove injection risk. Recommended practices include documenting trust transitions across components, using canaries and synthetic records to avoid test side effects, running multi-turn and obfuscated injection campaigns, and verifying chains from poisoned documents to observable state changes. It also details testing RAG pipelines via controlled document poisoning across metadata, OCR layers, and code comments, plus cross-tenant isolation checks on retrieved document IDs.

CSO OnlineSep 9, 10:00 UTC · 6d agoAI safety & security1