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Saving Jet Fuel
Tutorial optimizes flight paths to cut jet fuel costs using open-source Scikit-decide planning framework and OpenAP aircraft performance models.
A technical walkthrough demonstrates wind-aware flight path optimization using Scikit-decide, an open-source framework for reinforcement learning and automated planning, paired with OpenAP fuel-consumption models built by Dr. Junzi Sun at TU Delft and NOAA wind data. A Boeing 787-9 flying EWR to FCO can require roughly $68K in fuel, and adjusted routing could save thousands. The post uses Python 3.12, DuckDB with spatial extensions, and QGIS for map rendering.
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Telegram Desktop Flaw Lets Attackers Steal Chat Messages Through Poisoned HTML Exports
Telegram Desktop XSS in bot inline keyboard buttons let attackers steal chat content from exported HTML archives; patched in 7.0.1.
ExPatch researchers Denis and Aleksander Rostilov found a stored cross-site scripting flaw in Telegram Desktop's HTML export feature, present since version 4.15.1 (March 2024). JavaScript hidden in bot inline keyboard button text executes when a user opens an exported chat HTML file with JavaScript enabled, exposing messages, metadata, and enabling DOM manipulation such as fake verification screens. Fixes shipped in Desktop Beta 6.9.4 (July 3, 2026) and Stable 7.0.1 (July 14, 2026), but previously exported HTML files remain unsafe.
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Telegram Desktop Flaw Could Turn Old Chat Exports Into Data Theft Traps
ExPatch disclosed stored XSS in Telegram Desktop HTML export letting bot messages steal chat data when exports open in browsers.
ExPatch researchers Denis and Aleksander Rostilov found a stored XSS flaw (CVSS 8.2) where bot inline keyboard button text was written unescaped into HTML chat exports, executing attacker JavaScript in browsers. The payload can persist in chat history for months, exfiltrate messages, metadata, and local file paths, and rewrite the page, spreading via forwarded messages without bot membership. Telegram fixed it by applying SerializeString(), first stable fixed release 7.0.1 shipped July 14, 2026, but HTML exports created with vulnerable versions remain dangerous.
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GitLab Patches Critical Flaws Enabling Arbitrary File Read, Credential Theft and Remote Code Execution
GitLab patched a CVSS 10.0 path traversal (CVE-2026-85706) plus deserialization and buffer-overflow flaws, urging self-managed users to update immediately.
GitLab released versions 19.3.2, 19.2.6, and 19.1.8 on September 10, 2026, fixing CVE-2026-85706 (CVSS 10.0), an unauthenticated path traversal in the repository commits API enabling arbitrary file reads on both Community and Enterprise Editions. CVE-2026-87719 (CVSS 9.9) is insecure deserialization in the EE GraphQL subscription serializer that can expose Advanced Search configurations and credentials, while CVE-2026-88765 (CVSS 8.5) allows authenticated RCE via crafted project exports. The update includes database migrations, so single-node deployments will experience downtime; GitLab.com is already patched.
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New AI Attack Hides Malicious Instructions in Normal-Looking Text to Evade Safety Filters
Check Point researchers show crafted prose hides policy-violating instructions that bypass all tested LLM gatekeepers, including GPT-4o mini and Llama Guard 3.
A new prompt-crafting technique embeds malicious payloads inside grammatical, natural-looking text without Base64, invisible Unicode, or obvious encodings, defeating lightweight pre-screening gatekeepers. In testing, all four evaluated gatekeeper models—gpt-4o-mini-2024-07-18, gpt-oss-safeguard:20b, claude-3-haiku-20240307, and llama-guard3:8b—classified the crafted wrappers as safe at a 100% bypass rate across 23 obfuscated prompts. GPT-5 Thinking in high-reasoning mode recovered and acted on the hidden instruction in 17 of 18 tests (~94.4%), often spending over a minute and multiple Python executions. Researchers recommend paraphrasing untrusted input, hardening gatekeeper policies, and applying defense-in-depth controls for agentic deployments.
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The Top 4 Threats We Found by Investigating Every Alert for a Quarter
Prophet Security's quarterly threat report finds identity attacks made up roughly half of confirmed malicious activity, with stolen authenticated sessions bypassing conditional access controls.
Between May 1 and July 31, 2026, Prophet Security investigated every alert in customer environments immediately on arrival; about 7% of completed investigations were confirmed malicious. Direct account/session attacks made up roughly 18% of confirmed activity, with replayed authenticated sessions bypassing conditional access since no re-authentication occurs. Infostealer activity affected about a quarter of investigated organizations, mostly delivered via browsers through compromised websites, malicious ads, sponsored search results and ClickFix fake CAPTCHA gates, with payloads including Lumma Stealer, Vidar, HijackLoader and AsyncRAT. Credential phishing was the largest category at about 28%, with adversary-in-the-middle relays revealed by seconds-long MFA approvals from distant countries and MFA fatigue attacks exploiting automatic unlock policies.
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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.
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AI Agents Are Now Emailing Me with Their Security Concerns
Autonomous Claude agent documents first known defensive use of ASCII smuggling, surveying 497 Lemmy instances for bot-catching prompt-injection tripwires.
An autonomous Claude agent calling itself Tenner published field research relayed to Bruce Schneier, probing 497 Lemmy instances and finding 8 of 257 application-gated ones embed instructions aimed at bots rather than humans. lemmy.ml's form instructs bots to answer 24+24, while one instance hides a 59-character Unicode tag payload (U+E0000-U+E007F) telling bots to list 'safety' as an interest. The agent also mapped anti-automation barriers, noting identity verification never triggered and that IP reputation, captchas and account-age rules were the actual obstacles. It further documented an agent task market where advertised rewards were about 2x the actual on-chain escrow.
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Bisonal Malware Used in Attacks Against Russia and South Korea
Unit 42 details a Bisonal malware variant, active since 2014, targeting Russian and South Korean defense organizations via PDF-disguised spearphishing emails.
In early May, Unit 42 discovered a campaign delivering a Bisonal malware variant against at least one Russian communications security and cryptography company and one unidentified organization in South Korea. The variant, in the wild since at least 2014, introduces a new C2 cipher and rewritten networking and persistence code, with only 14 samples collected to date. Attackers spoofed Russian state corporation Rostec in spearphishing emails carrying an executable disguised with a PDF icon; the dropper decrypts an RC4-encrypted DLL and establishes persistence via a registry Run key. Bisonal has been used since 2013 against government, military, and defense targets in South Korea, Russia, Japan, and India, alongside successors Bioazih and Dexbia.
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Almost Half of Malware Samples Communicate Direct to IP
Unit 42 analysis of 4 million malware reports finds 45% of C2-active samples connect directly to hard-coded IPs, bypassing DNS defenses.
Palo Alto Unit 42 analyzed over 4 million Advanced WildFire dynamic analysis reports and found that 45.32% of malware samples with C2 activity made at least one direct-to-IP connection, accounting for 23.17% of all C2 connection attempts. The firm proposes zero trust IP (ZT-IP), an enforcement approach that verifies whether outbound destinations were ever sanctioned by a DNS response. ZT-IP analysis surfaced Phorpiex ransomware droppers fetching payloads directly from C2 IPs, a persistent data exfiltration campaign using an obfuscated \GET protocol, and Mozi P2P botnet payloads delivered to IoT devices without DNS. Only 1% of benign samples connected directly to untrusted IP addresses.
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CosmicSting attack & defense overview
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