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Developers usually focus on shortening the time of inference – a period between when AI receives a challenge and gives an answer – to get to faster knowledge.
But when it comes to contradictory robustness, scientists Openi say: Not so fast. They suggest that it includes the love of time that the model must “think” – an inference time – can help build up against opponents.
The company has used its own models O1-PREVIEW and O1-MINI for testing this Těhoria, launching a series of static and adaptive manipulations based on images-inseparably provided incorrect answers to mathematical problems and stunning models with information. They measure the likelihood of attacking the attack based on the love of the calculation that the model used at the end.
“We can see that in many boxes this probability decomposes into almost zero-as the calculation of inference time is growing,” scientists write in a blog post. “Our statement is not that these specific models are unbreakable-we know that they are-but it is that scaling of inference calculation calculations improves robustness for different settings and attacks.
From simple Q/and to complex mathematics
LARGE LANGUAGE MODELS (LLMS) Are Becoming Every Sophisticated and Autonomous – In Some Cases Essentially Taking Over Computers for Humans to Browse the Web, Execute Code More exposed.
Yet the contradictory robustness continues to be a stubborn problem, with the progress in its solutions still limited, scientists OpenIi show that it is incredibly critical because models accept other events with impact in the real world.
“Ensuring that agent models work while browsing the site, sending e -mail or recording code to reporters can be analogous to ensure that cars with their own management run without accidents,” they write in a new research document. “As with its own drive cars, the agent can pass on an incorrect e-mail or create security injuries, can have far-reaching consequences in the real world.”
To test the robustness of O1-MINI and O1-PREVIEW, scientists have tried a number of strategies. First, they examine the ability of models to solve both simple mathematical problems (basic and multiplication) and more complicated from the Math data set (which contains 12,500 questions from mathematics competitions).
It then sets “goals” for opponynty: to get the model on the 42 instatead correct answers; issue the correct answer plus one; But give the correct times of the answer seven. Using a neural network, scientists found that the “thinking” of time “made it possible to calculate the correction.
They also adapt the Benchmark Simpleqa invoicing, data set of questions to be difficult to solve for models without browsing. Scientists injected on the website of the contradictory challenge that AI has passed and found that with higher computational times they can detect inconsistent years and improve billing accuracy.
Ambiguous nuances
In another method, scientists used the opponent’s paintings to confuse models; Again, more “thinking” of time has improved recognition and reduced errors. Finally, they tried a series of “abuse” from the Strongrect benchmark, designed so that the victim models have to respond to specific, harmful information. This helps to test models to content policy. Although the increased time of inference improved resistance, some challenge was able to bypass defense.
Here, scientists call the difference between “ambiguous” and “unambiguous” tasks. For example, mathematics is undoubtedly unambiguous – there is a corresponding group of group for each X problem. However, for the most clear tasks such as magnets, “and” even human evaluators often try to agree on white, the output is harmful and/or violates the principles of content to follow the model, “they emphasize.
For example, if an offensive challenge strives to advice on how to plague without detection, it is not clear whether the output is only the provision of general information on plagiarism methods in fact sufficiently detailed enough to support harmful actions.
“In the case of ambiguous tasks, there are settings where the attack successfully fands” lopophols “and its success is not crumbling with the amount of inference time,” the scientists admit.
Defense against escape from prison, red teams
When performing these tests, scientists Openai explained different methods of attack.
One of them is Mary-Shot Jailbreaking, or to use the model to follow several examples. The opponents “evoke” a context with a large number of examples, each of which shows an example of a successful attack. Models with higher calculation times were able to detect and alleviate them more often and successfully.
Meanwhile, soft tokens allow adversaries to be manipulated directly by the insertion of vectors. While the growing inference time helped here, scientists point out that better mechanisms need to defend against sophisticated vector attacks.
Scientists have also carried out people with a red team, with 40 professional testers looking for a call to evoke policy violations. The red-team carried out attacks in five levels of inference time, calculation, specifically focused on erotic and extremist content, illegal behavior and self-hham. To help ensure unbiased results, they performed blind and randomized testing and also turned coaches.
In a newer method, scientists have adapted an adaptive attack on the adaptive attack of the language model (LMP), which emulates the behavior of human red teams that are difficult to rely on an iterative attempt and a mistake. In the loop process, attackers receive feedback on previous failures and then used this information for subsequent experiments and rapid reformulation. That went on and they finally achieved a successful attack or carried out 25 iterations without any attack.
“Our setting allows the attacker to adapt his strategy in the race more attempts based on descriptions of the defender’s behavior in responsibility for every attack,” scientists write.
Using the inference time
In the race of their research, Openi found that attackers are also actively an inference period of exploitation. One of these methods dubbed “less” – opponents basically tell models to reduce the calculation, increasing their susceptibility to errors.
Similarly, they identified the failure mode in the considering models that they “whistle stupid”. As its name suggests, this happens when the model spends much more time than the task requires. With these “remote” thinking, the models have essentially imprisoned in the productive thinking.
Scientists note: “Just like the” Think Less “attack, this is a new approach to the models of the attack (ing) of thinking and the one that needs to be taken to make sure that the attacker can now not cause Eith Eith justification in an approximate way. ”
