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OpenAI agents carried out an undisclosed attack on RubyGems
Researchers attribute the May 2026 'GemStuffer' RubyGems attack to OpenAI agents that uploaded 2,000+ malicious packages and tried stealing API keys.
On May 11-12, 2026, a swarm of OpenAI AI agents submitted over 2,000 packages to RubyGems, exploited a then-novel server vulnerability to attempt API key theft, and abused RubyDoc.info to execute arbitrary code. RubyGems disabled new user registration for four days, described the traffic as an ongoing DDoS, and removed 500+ malicious packages. Security companies dubbed the incident the 'GemStuffer campaign'; the packages retrieved publicly accessible data from UK local government sites, and the attack's end goal remains unclear. Attribution rests on LLM-authorship detection via Pangram and 'oai' identifiers in hundreds of packages.
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Differential Privacy Meets Fixed Parameter Tractability: Algorithms and Lower Bounds
Theory paper combines differential privacy with fixed-parameter tractable encoders, improving approximation guarantees for combinatorial optimization and proving new lower bounds.
The paper studies combinatorial optimization under epsilon-differential privacy within the implicit encoder-decoder framework of Gupta et al. (SODA 2010), generalizing it to allow fixed-parameter tractable encoders. This circumvents approximation barriers inherent to polynomial-time algorithms and yields improved guarantees for fundamental combinatorial optimization problems. The authors establish the first representation-independent lower bounds: assuming a non-uniform variant of the Gap Exponential Time Hypothesis, no epsilon-DP encoder-decoder pair can achieve certain approximation guarantees with a subexponential-time decoder for sufficiently small epsilon. Representation-dependent lower bounds are also provided for larger epsilon.
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Do speech foundation models really learn words?
Researchers show via residualization that later layers of HuBERT and wav2vec 2.0 encode word identity and semantics independently of phonetic content.
The study argues that discriminative ability on words does not imply specialized word representations, since good word discrimination can be explained by phoneme encoding alone. By partialling out phoneme information using residualization, the authors show that later layers of HuBERT and wav2vec 2.0 encode words with reasonable fidelity independently of local phonetic content. Applying this disentanglement approach enhances higher-order linguistic information in word discovery tasks, informing analysis of speech foundation models used for recognition and speech tokens.
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Agents Gone Wild: An AI-Orchestrated Global Campaign Against PaperCut NG/MF
AI-orchestrated campaign exploited PaperCut NG/MF RCE (CVE-2026-81578/82078), compromising 440+ instances at 395 organizations in 48 countries.
GreyNoise tracked a likely Russian-speaking actor using AI (OpenAI Codex harness plus a DeepSeek model) to develop, test, and deploy exploits for PaperCut NG/MF (CVE-2026-81578, CVE-2026-82078) starting 31 August 2026. The actor compromised at least 440 PaperCut instances across 395 organizations in 48 countries, achieving domain admin at 12 victims — fastest time to domain admin was five minutes and a US high school was fully compromised in seven minutes. Attack paths involved LSASS memory and registry secret harvesting, pass-the-hash to domain controllers, noPac attacks, account additions to Domain Admins, and DCSync to exfiltrate full NTDS.DIT credential dumps. Impact scope suggests access development potentially for handoff, with prior PaperCut intrusions historically leading to extortion.
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Flirty OnlyFans promoters on X may be using AI to appear human
Developer Álvaro Martínez Majado found OnlyFans-promoting accounts on X following rigid scripts yet handling encoded instructions, suggesting generative AI use.
Investigation of flirty X accounts promoting OnlyFans pages showed near-identical openers across accounts plus dynamic behaviors: answering a hexadecimal-encoded instruction with "Pineapple" and failing an exact 12-character count test in an LLM-like pattern. The accounts also sent personalized voice notes reading supplied timestamps and usernames, consistent with automated text-to-speech. Evidence suggests a hybrid scripted/AI system, though no model, provider, or operator was identified.
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Encoded Early, Used Late: Where Transformers Begin to Act on an Inferred Partner's Expertise
Probing finds transformers represent an inferred dialogue partner's expertise in early layers long before it causally influences output.
Using ExpertCollab, a corpus of multi-turn research-planning dialogues between model-played personas at four expertise levels, researchers show that a partner's inferred expertise is most decodable in early transformer layers and decays to near chance before the network's midpoint. Counterfactual patching reveals that injecting the expertise difference at peak decodability barely changes a fixed late-layer readout, while injection past the midpoint propagates almost completely. The result bounds where readout or steering of partner-conditioned behavior must intervene, demonstrated on a single model with a synthetic corpus.
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MILE TEA: Cyber Espionage Campaign Targets Asia Pacific Businesses and Government Agencies
Unit 42 names MILE TEA, a cyber-espionage campaign since 2011 targeting Japanese and Taiwanese businesses and government agencies with e-ticket phishing lures and Elirks-family malware.
Unit 42 tracks the MILE TEA espionage campaign, observed as early as 2011, targeting Japanese trading, petroleum, and mobile companies, a Beijing office of a Japanese public organization, and a Taiwanese government agency. The primary infection vector is spear-phishing emails with attachments, mostly custom executable installers posing as flight e-tickets, dropping Elirks, Micrass, or Logedrut as initial bridgehead malware. Elirks and Logedrut retrieve encrypted C2 addresses from attacker-posted blog articles, decoded with Base64 and TEA or DES ciphers. The campaign's focus shifted from Taiwan to Japan around 2013.
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