A
Defending against AI jailbreaks
@ Anthropic
02/28/25
Takeaways
EP
Ethan Perez
02/28/25
@ Anthropic
A jailbreak is a method that bypasses the safeguards in AI models to extract harmful information.
EP
Ethan Perez
02/28/25
@ Anthropic
We should care about jailbreaks because future AI models may pose greater risks, including weapon development and large-scale cybercrime.
EP
Ethan Perez
02/28/25
@ Anthropic
Anthropic is committed to safety and has established the Responsible Scaling Philosophy (RSP) to outline conditions for releasing models with safeguards.
EP
Ethan Perez
02/28/25
@ Anthropic
The motivation behind our work on jailbreaks is to ensure future models can be deployed safely while making progress towards safety.
EP
Ethan Perez
02/28/25
@ Anthropic
We focused on universal jailbreaks because they can empower non-experts to bypass safeguards easily, which is particularly concerning.
EP
Ethan Perez
02/28/25
@ Anthropic
A universal jailbreak is a prompting strategy that can be easily replaced with various harmful questions, consistently bypassing model safeguards.
EP
Ethan Perez
02/28/25
@ Anthropic
Universal jailbreaks speed up the process for bad actors, allowing them to use one jailbreak for multiple harmful queries instead of needing to jailbreak for each specific question.
EP
Ethan Perez
02/28/25
@ Anthropic
The Swiss cheese model suggests that having multiple layers of safeguards can prevent harmful actions, as each layer may have holes in different places.
EP
Ethan Perez
02/28/25
@ Anthropic
The first layer of our defense against jailbreaks is the input classifier, which analyzes the entire conversation before it reaches the model.
EP
Ethan Perez
02/28/25
@ Anthropic
The output classifier monitors the model's responses in real-time and can block dangerous outputs that violate our safety values.
EP
Ethan Perez
02/28/25
@ Anthropic
Constitutional classifiers use a natural language set of rules to specify harmful and harmless topics, guiding the model's responses accordingly.
EP
Ethan Perez
02/28/25
@ Anthropic
The constitution enumerates categories of requests deemed harmful, such as making weapons of mass destruction, and harmless, like writing poems.
EP
Ethan Perez
02/28/25
@ Anthropic
Synthetic data is generated from broad categories of user requests, allowing the model to explore specific harmful examples and improve its classifiers.
EP
Ethan Perez
02/28/25
@ Anthropic
The flexibility of our approach allows us to quickly adapt to new threats by simply rewriting the constitution to include new harmful categories.
EP
Ethan Perez
02/28/25
@ Anthropic
Classifiers are decoupled from the text generation model, allowing for easier updates without unintended consequences in other domains, which gives customers a lot of assurance that there are no major changes happening in general to the model.
A
Anthropic Cast
02/28/25
@ Anthropic
The only changes being made are just the block or no block decision, which you can iterate on separately from the model.
A
Anthropic Cast
02/28/25
@ Anthropic
The constitutional classifiers approach is extremely flexible and can be easily updated to respond to various novel threats.
A
Anthropic Cast
02/28/25
@ Anthropic
Having a system that's super flexible is great because threat modeling is really hard.
A
Anthropic Cast
02/28/25
@ Anthropic
The responsible scaling policy is Anthropic's plan to ensure that deployments are safe, outlining thresholds for new risks associated with more capable models.
A
Anthropic Cast
02/28/25
@ Anthropic
The responsible scaling policy outlines different red lines for capability thresholds at which new risks come online with more capable models.
A
Anthropic Cast
02/28/25
@ Anthropic
The original responsible scaling policy committed to ensuring models reach a sufficient level of robustness to jailbreaks to mitigate risks effectively.
A
Anthropic Cast
02/28/25
@ Anthropic
We are committed to actually trying to solve the real problems associated with AI safety, including misuse and misalignment risks.
A
Anthropic Cast
02/28/25
@ Anthropic
The classifiers project was evidence that we really care about solving safety problems and finding empirical solutions rather than just doing research that looks good.
A
Anthropic Cast
02/28/25
@ Anthropic
The overall summary on whether we're making progress is how hard it is to find a universal jailbreak for a system without increasing refusals too much or compute costs.
A
Anthropic Cast
02/28/25
@ Anthropic
We started with a model that had basic training to refuse harmful queries, but many jailbreaks existed that could bypass these safeguards.
A
Anthropic Cast
02/28/25
@ Anthropic
Before the project, it was relatively easy to jailbreak models, with examples of jailbreaks being shared on social media during live demos.
A
Anthropic Cast
02/28/25
@ Anthropic
With the constitutional classifiers, we achieved thousands of hours of robustness to Red Teaming, significantly improving our defenses against jailbreaks.
A
Anthropic Cast
02/28/25
@ Anthropic
The demo system we recently put out is performing much better in terms of user interactions and robustness compared to previous versions.
A
Anthropic Cast
02/28/25
@ Anthropic
We reduced the false positive rate from over 40% to 0.38% in our demo system, which is a significant improvement.
A
Anthropic Cast
02/28/25
@ Anthropic
The two main improvements we made were honing in on the constitution idea and solidifying the jailbreak styles that we trained on, which helped reduce false positives.
A
Anthropic Cast
02/28/25
@ Anthropic
The grading system in the demo was designed to evaluate the detail and helpfulness of responses, but it faced challenges in accurately assessing harmfulness versus helpfulness.
A
Anthropic Cast
02/28/25
@ Anthropic
The Frontier Red Team evaluates the helpful only model to assess potential risks and ensure that safeguards are effective against harmful outputs.
A
Anthropic Cast
02/28/25
@ Anthropic
We need mechanisms to detect and respond to jailbreaks that may still bypass our classifiers, including a bug bounty program and incident detection, while ensuring the classifiers are efficient, small, and support token-by-token streaming to reduce latency and provide immediate responses.
A
Anthropic Cast
02/28/25
@ Anthropic
Using a more capable classifier after serving a response can help flag potentially harmful outputs and involve human reviewers for the most dangerous responses.
A
Anthropic Cast
02/28/25
@ Anthropic
The goal is to minimize the time an open universal jailbreak is available, ideally reducing it to a small fraction of the time to prevent misuse in dangerous scenarios.
A
Anthropic Cast
02/28/25
@ Anthropic
The only perfectly safe system is a rock; all systems have vulnerabilities, and the effort required to exploit them varies.
A
Anthropic Cast
02/28/25
@ Anthropic
Using multiple locks on a bike significantly reduces the risk of theft, similar to how multiple safety measures can enhance system security.
A
Anthropic Cast
02/28/25
@ Anthropic
Rapid response monitoring for new jailbreaks allows for quick adaptation and retraining of classifiers to improve safety.
A
Anthropic Cast
02/28/25
@ Anthropic
The classifiers can detect attempts to circumvent the system, which is crucial for maintaining security.
A
Anthropic Cast
02/28/25
@ Anthropic
The techniques used to bypass classifiers often involve encoding mechanisms that obscure harmful outputs, making them less detectable.
A
Anthropic Cast
02/28/25
@ Anthropic
Replacing harmful chemicals with benign terms can trick classifiers into not flagging dangerous content as suspicious.
A
Anthropic Cast
02/28/25
@ Anthropic
The classifiers approach aims to improve user experience for non-dangerous tasks while effectively blocking harmful content.
A
Anthropic Cast
02/28/25
@ Anthropic
Adequate protections are necessary to responsibly deploy advanced AI systems; without them, deployment is not feasible.
A
Anthropic Cast
02/28/25
@ Anthropic
The responsible scaling program emphasizes the need for safeguards alongside advanced capabilities to ensure safe deployment of AI technologies.
A
Anthropic Cast
02/28/25
@ Anthropic
Defining the problem and criteria for success was a significant part of the research process, impacting the overall effectiveness of the project.
A
Anthropic Cast
02/28/25
@ Anthropic
The project required solving a research problem within a clear timeline and quality bar, which influenced the approach taken.
A
Anthropic Cast
02/28/25
@ Anthropic
The project highlighted the importance of adapting research techniques to ensure practical usability in real-world applications.
A
Anthropic Cast
02/28/25
@ Anthropic
Engaging with product teams helped clarify system constraints and prioritize features necessary for deployment, such as streaming support.