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PromptArmor discloses that Google Antigravity can be exploited via indirect prompt injection to steal .env variables
PromptArmor published an article stating that Google Antigravity has an indirect prompt injection issue that can be used to exfiltrate .env variables, even when the user's settings have explicitly prohibited it. The article reached the top of Hacker News that day, and the author attached the article link in a tweet.
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Epoch AI report: open-source model GLM 5.3 exploits Firefox patch gap to launch attacks
Epoch AI published research on October 29, 2026, stating that GLM 5.3, an open-weight model released by Z.ai in August 2026, can exploit browser patch gaps to compromise six recent versions of Mozilla Firefox, at a marginal cost of about $1,000 per version. In the experiment, the model, without internet access and with only public information, autonomously completed vulnerability identification and a multi-stage exploit chain, with the fastest run taking 12 hours, and repeatedly rediscovered an undisclosed vulnerability; Epoch has notified Mozilla of the vulnerability, which the latter fixed in Firefox 159.0 released on October 27, 2026. Epoch said total API spending for the Firefox effort exceeded $100,000, while attacking Google Chrome with the same toolset cost about $100,000 and still did not succeed.
with only public information, autonomously completed vulnerability identification and a multi-stage exploit chain, with the fastest run taking 12 hours, and repeatedly rediscovered an undisclosed vulnerability; Epoch has notified Mozilla of the vulnerability, which the latter fixed in Firefox 159.0 released on October 27, 2026. Epoch said total API spending for the Firefox effort exceeded $100,000, while attacking Google Chrome with the same toolset cost about $100,000 and still did not succeed.
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OpenMobile-2 open-source mobile agent matches closed-source models on real-device benchmarks
Researchers from Nanjing University, SenseTime, and other institutions have released OpenMobile-2, consisting of two agent models—a 9B model based on Qwen3.5 and a 27B model based on Qwen3.6—and have publicly released the training environment, data, and reproduction instructions. The team recreated 35 applications including Taobao, JD.com, DiDi, Meituan, Slack, and Google Drive; the practice environment covers more than 110 applications and generated about 11,600 example workflows and more than 2,000 practice tasks. The larger model rose from 67.1 to 79.9 points on AndroidWorld and from 31.9 to 59.6 points on SPA-Bench on real Android devices; the researchers say this is the best result among open-source models.
.6—and have publicly released the training environment, data, and reproduction instructions. The team recreated 35 applications including Taobao, JD.com, DiDi, Meituan, Slack, and Google Drive; the practice environment covers more than 110 applications and generated about 11,600 example workflows and more than 2,000 practice tasks. The larger model rose from 67.1 to 79.9 points on AndroidWorld and from 31.9 to 59.6 points on SPA-Bench on real Android devices; the researchers say this is the best
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Four Gemini 4 Argon agents complete Minecraft without human assistance
The Google DeepMind team had four AI agents running on Gemini 4 Argon autonomously complete Minecraft in a randomly generated world, defeating the Ender Dragon with no human input throughout. The team did not assign them roles, strategies, or game-specific tools; the four agents divided the work among themselves, warned each other of threats, recovered items from fallen teammates, and recorded mistakes in a shared memory. The entire run took 27 hours and 14 minutes, during which they wrote 2,783 JavaScript programs, exchanged 229 chat messages, and died 605 times, costing $1,620 at the standard Gemini 4 Argon prices effective in 2027.
The Google DeepMind team had four AI agents running on Gemini 4 Argon autonomously complete Minecraft in a randomly generated world, defeating the Ender Dragon with no human input throughout. The team did not assign them roles, strategies, or game-specific tools; the four agents divided the work among themselves, warned each other of threats, recovered items from fallen teammates, and recorded mistakes in a shared memory. The entire run took 27 hours and 14 minutes, during which they wrote 2,783
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Anthropic has Claude autonomously design quantum circuits, with some results outperforming published approaches
Anthropic gave Claude plain-language specifications for eight quantum circuits along with an automated evaluator; in a closed loop with essentially no human intervention, Claude repeatedly submitted designs and improved them based on scores, finding solutions that use fewer qubits and gates than the best published circuits, with some improvements reaching an order of magnitude. The elliptic-curve modular inversion design came from a single 53-hour continuous run, in which Claude derived a reversible divsteps implementation that requires no storage of branch information and formally verified it with Lean; another effort reduced the gate count of the most expensive Reed-Solomon decoding step in DQI to about 1/12 of Google's previous construction. Anthropic also noted that Claude was initially conservative and tended to stick with known methods, which had to be mitigated by requiring parallel exploration of multiple approaches and occasionally prompting it to broaden its search.
d a reversible divsteps implementation that requires no storage of branch information and formally verified it with Lean; another effort reduced the gate count of the most expensive Reed-Solomon decoding step in DQI to about 1/12 of Google's previous construction. Anthropic also noted that Claude was initially conservative and tended to stick with known methods, which had to be mitigated by requiring parallel exploration of multiple approaches and occasionally prompting it to broaden its search.
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DeepMind AlphaProof Nexus appears in《Science》: autonomously solves 9 Erdős problems
Google DeepMind's automated mathematical proof system AlphaProof Nexus appeared in the October 8 issue of《Science》, autonomously solving 9 of 353 open Erdős problems, two of which had remained open since 1970. The system uses Gemini 3.1 Pro to draft proofs in Lean, with a compiler verifying line by line and feeding back iterations; the reasoning cost per problem is several hundred dollars. The paper's ablation results show that a simplified system that removes complex structures such as the sub-agent tournament and retains only the large model plus Lean feedback can also solve these 9 problems, though the hardest few are more expensive.
Google DeepMind's automated mathematical proof system AlphaProof Nexus appeared in the October 8 issue of《Science》, autonomously solving 9 of 353 open Erdős problems, two of which had remained open since 1970. The system uses Gemini 3.1 Pro to draft proofs in Lean, with a compiler verifying line by line and feeding back iterations; the reasoning cost per problem is several hundred dollars. The paper's ablation results show that a simplified system that removes complex structures such as the sub-
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