Intellectia News

Identity management firm Okta said Friday it has patched a critical authentication bypass vulnerability that affected customers using usernames longer than 52 characters in its AD/LDAP delegated authentication service. The flaw, introduced on July 23 and fixed October 30, allowed attackers to authenticate using only a username if they had access to a previously cached key. The bug stemmed from Okta's use of the Bcrypt algorithm to generate cache keys from combined user credentials. The company switched to PBKDF2 to resolve the issue and urged affected customers to audit system logs. Read more of this story at Slashdot.

California signed an agreement with major airlines to increase the use of sustainable aviation fuels, aiming to reach 200 million gallons by 2035 or about 40% of the state's air travel demand. The Hill reports: The California Air Resources Board (CARB) and Airlines for America (A4A) -- an industry trade group representing almost a dozen airlines -- pledged to increase the availability of sustainable aviation fuels statewide. Sustainable aviation fuels -- lower-carbon alternatives to petroleum-based jet fuels -- are typically made from nonpetroleum feedstocks, such as biomass or waste. At a San Francisco International Airport ceremony Wednesday, the partners committed (PDF) to using 200 million gallons of such fuels by 2035 -- an amount estimated to meet about 40 percent of travel demand within the state at that point, according to CARB. That quantity also represents a more than tenfold increase from current usage levels of these fuels, the agency added. Among A4A member airlines are Alaska Airlines, American Airlines, Atlas Air Worldwide, Delta Air Lines, FedEx, Hawaiian Airlines, JetBlue Airways, Southwest Airlines, United Airlines and UPS, while Air Canada is an associate member. To achieve the 2035 goals, CARB and A4A said they plan to work together to identify, assess and prioritize necessary policy measures, such as incentivizing relevant investments and streamlining the permitting processes. A Sustainable Aviation Fuel Working Group, which will include government and industry stakeholders, will meet annually to both discuss progress and address barriers toward meeting these goals, the partners added. A public website will display updated information about the availability and use of conventional and sustainable fuels across California, while also providing details about state policies, according to the agreement. Read more of this story at Slashdot.

Waymo is advancing autonomous driving with a new training model for its robotaxis built on Google's multimodal large language model (MLLM) Gemini. The Verge reports: Waymo released a new research paper today that introduces an "End-to-End Multimodal Model for Autonomous Driving," also known as EMMA. This new end-to-end training model processes sensor data to generate "future trajectories for autonomous vehicles," helping Waymo's driverless vehicles make decisions about where to go and how to avoid obstacles. But more importantly, this is one of the first indications that the leader in autonomous driving has designs to use MLLMs in its operations. And it's a sign that these LLMs could break free of their current use as chatbots, email organizers, and image generators and find application in an entirely new environment on the road. In its research paper, Waymo is proposing "to develop an autonomous driving system in which the MLLM is a first class citizen." The paper outlines how, historically, autonomous driving systems have developed specific "modules" for the various functions, including perception, mapping, prediction, and planning. This approach has proven useful for many years but has problems scaling "due to the accumulated errors among modules and limited inter-module communication." Moreover, these modules could struggle to respond to "novel environments" because, by nature, they are "pre-defined," which can make it hard to adapt. Waymo says that MLLMs like Gemini present an interesting solution to some of these challenges for two reasons: the chat is a "generalist" trained on vast sets of scraped data from the internet "that provide rich 'world knowledge' beyond what is contained in common driving logs"; and they demonstrate "superior" reasoning capabilities through techniques like "chain-of-thought reasoning," which mimics human reasoning by breaking down complex tasks into a series of logical steps. Waymo developed EMMA as a tool to help its robotaxis navigate complex environments. The company identified several situations in which the model helped its driverless cars find the right route, including encountering various animals or construction in the road. [...] But EMMA also has its limitations, and Waymo acknowledges that there will need to be future research before the model is put into practice. For example, EMMA couldn't incorporate 3D sensor inputs from lidar or radar, which Waymo said was "computationally expensive." And it could only process a small amount of image frames at a time. There are also risks to using MLLMs to train robotaxis that go unmentioned in the research paper. Chatbots like Gemini often hallucinate or fail at simple tasks like reading clocks or counting objects. Read more of this story at Slashdot.