English translation is being prepared.
Matched fieldsTitle
←Search results
Search “Google”
English translation is being prepared.
Matched fieldsTitle
English translation is being prepared.
Matched fieldsTitle
English translation is being prepared.
Matched fieldsTitle
English translation is being prepared.
Matched fieldsTitle
English translation is being prepared.
Matched fieldsTitle
PromptArmor says Google Antigravity can be exploited via indirect prompt injection to leak .env variables
PromptArmor published an article on November 26, 2025, stating that Google Antigravity has an indirect prompt injection issue that can be used to exfiltrate .env variables, even if the user has explicitly prohibited it in settings. The article topped Hacker News that day, and the author attached the article link in a tweet. There are currently no subsequent developments or response from Google.
Matched fieldsTitle
PromptArmor says Google Antigravity can be exploited via indirect prompt injection to leak .env variables
PromptArmor published an article on November 26, 2025, stating that Google Antigravity has an indirect prompt injection issue that can be used to exfiltrate .env variables, even if the user has explicitly prohibited it in settings. The article topped Hacker News that day, and the author attached the article link in a tweet. There are currently no subsequent developments or response from Google.
Matched fieldsTitle
Epoch AI releases study on GLM 5.3 exploiting Firefox patch window
On October 29, 2026, Epoch AI released a study stating that GLM 5.3, an open-weight model released by Z.ai in August 2026, was able to exploit the patch gap window in browsers to compromise six recent versions of Mozilla Firefox, at a marginal cost of about $1,000 per version. Experiments showed that the model, without internet access and with only publicly available information, autonomously completed vulnerability identification and a multi-stage exploitation chain, with the fastest run taking 12 hours, and repeatedly rediscovered an undisclosed vulnerability. Epoch AI has notified Mozilla of the vulnerability, which the latter fixed in Firefox 159.0, released on October 27, 2026. Epoch AI also stated that the total API cost for targeting Firefox exceeded $100,000, while using the same set of tools to attack Google Chrome cost about $100,000 and was still unsuccessful.
autonomously completed vulnerability identification and a multi-stage exploitation chain, with the fastest run taking 12 hours, and repeatedly rediscovered an undisclosed vulnerability. Epoch AI has notified Mozilla of the vulnerability, which the latter fixed in Firefox 159.0, released on October 27, 2026. Epoch AI also stated that the total API cost for targeting Firefox exceeded $100,000, while using the same set of tools to attack Google Chrome cost about $100,000 and was still unsuccessful.
Matched fieldsSummary
Research team releases open-source mobile phone agent model OpenMobile-2
Researchers from Nanjing University, SenseTime and other institutions have released an open-source mobile phone agent model, OpenMobile-2, comprising two agent models: a 9-billion-parameter model based on Qwen3.5 and a 27-billion-parameter model based on Qwen3.6, along with the complete training environment, data and reproduction instructions. The team rebuilt 35 applications including Taobao, JD.com, Didi, Meituan, Slack and Google Drive, with the practice environment covering more than 110 applications, generating about 11,600 example workflows and more than 2,000 practice tasks. The larger model rose from 67.1 to 79.9 on AndroidWorld and from 31.9 to 59.6 on SPA-Bench on real Android devices, which the researchers say is the best result among open-source models. There are currently no further developments.
d on Qwen3.6, along with the complete training environment, data and reproduction instructions. The team rebuilt 35 applications including Taobao, JD.com, Didi, Meituan, Slack and Google Drive, with the practice environment covering more than 110 applications, generating about 11,600 example workflows and more than 2,000 practice tasks. The larger model rose from 67.1 to 79.9 on AndroidWorld and from 31.9 to 59.6 on SPA-Bench on real Android devices, which the researchers say is the best result
Matched fieldsSummary
Four Gemini 4 Argon agents complete Minecraft without human intervention
The Google DeepMind team had four AI agents running on Gemini 4 Argon autonomously complete Minecraft in a randomly generated world, killing 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 a total of 2,783 JavaScript programs, exchanged 229 chat messages, and died 605 times, costing $1,620 at the standard Gemini 4 Argon pricing that takes effect in 2027.
Matched fieldsSummary
View story:Four Gemini 4 Argon agents complete Minecraft without human intervention
Anthropic announces research on Mythos 5 autonomously designing quantum circuits
On October 17, 2026, Anthropic published a study: when given only a pure natural-language specification and an automated evaluator, Mythos 5 essentially without human intervention repeatedly submitted and improved quantum circuit designs in a closed loop, finding circuits that use fewer qubits and gates than the best previously published solutions, with some improvements reaching an order of magnitude. The elliptic-curve modular inversion design came from a single 53-hour continuous run, in which the model 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 Reed-Solomon decoding step, the most expensive step in DQI, to about 1/12 of a construction previously made by Google. Anthropic also noted that the model was initially conservative and tended to follow known methods, which needed to be mitigated by requiring parallel exploration of multiple approaches and occasionally prompting it to broaden its search. Subsequent reports said the research came from the STEM Fellows program and gave differing formulations, such as a 17-fold reduction in gate count for the divsteps task.
verified it with Lean; another effort reduced the gate count of the Reed-Solomon decoding step, the most expensive step in DQI, to about 1/12 of a construction previously made by Google. Anthropic also noted that the model was initially conservative and tended to follow known methods, which needed to be mitigated by requiring parallel exploration of multiple approaches and occasionally prompting it to broaden its search. Subsequent reports said the research came from the STEM Fellows program an
Matched fieldsSummary
English translation is being prepared.
Matched fieldsSummary
Chollet says Argon performs similarly to Astra on ARC-AGI-3
François Chollet commented that Gemini 4 Argon performs similarly to Astra on ARC-AGI-3, with Google returning to the frontier. On the same day, ARC Prize published the verified results for Gemini 4 Argon on the ARC-AGI series: ARC-AGI-3 scored 97.5% under the provider adapter harness at a cost of $4.8K; under the standard harness it scored 49.8% at a cost of $5.8K. ARC-AGI-2 scored 91.3% at $0.32 per task; ARC-AGI-1 scored 98.5% at $0.16 per task. ARC Prize said Google DeepMind is the second frontier AI lab to exceed 95% on ARC-AGI-3, and Argon's 97.5% is second only to GPT-6 Astra's 99.9%. The standard harness test could not complete all 55 games due to API issues; the high-reasoning setting completed only 19 games, so no score was published.
François Chollet commented that Gemini 4 Argon performs similarly to Astra on ARC-AGI-3, with Google returning to the frontier. On the same day, ARC Prize published the verified results for Gemini 4 Argon on the ARC-AGI series: ARC-AGI-3 scored 97.5% under the provider adapter harness at a cost of $4.8K; under the standard harness it scored 49.8% at a cost of $5.8K. ARC-AGI-2 scored 91.3% at $0.32 per task; ARC-AGI-1 scored 98.5% at $0.16 per task. ARC Prize said Google DeepMind is the second fr
Matched fieldsSummary