Tailoring evaluations for adaptive attacks
Baseline mitigations showed promise against basic, non-adaptive attacks, significantly reducing the attack success rate. Nevertheless, malicious actors increasingly use adaptive attacks which can be specifically designed to evolve and adapt with ART to avoid the defense being tested.
Successful baseline defenses like Spotlighting or Self-reflection became much less effective against adaptive attacks learning take care of and bypass static defense approaches.
This finding illustrates a key point: counting on defenses tested only against static attacks offers a false sense of security. For robust security, it’s critical to guage adaptive attacks that evolve in response to potential defenses.
Constructing inherent resilience through model hardening
While external defenses and system-level guardrails are vital, enhancing the AI model’s intrinsic ability to acknowledge and disrespect malicious instructions embedded in data can be crucial. We call this process ‘model hardening’.
We fine-tuned Gemini on a big dataset of realistic scenarios, where ART generates effective indirect prompt injections targeting sensitive information. This taught Gemini to disregard the malicious embedded instruction and follow the unique user request, thereby only providing the proper, protected response it should give. This enables the model to innately understand handle compromised information that evolves over time as a part of adaptive attacks.
This model hardening has significantly boosted Gemini’s ability to discover and ignore injected instructions, lowering its attack success rate. And importantly, without significantly impacting the model’s performance on normal tasks.
It’s vital to notice that even with model hardening, no model is totally immune. Determined attackers might still find recent vulnerabilities. Due to this fact, our goal is to make attacks much harder, costlier, and more complex for adversaries.
Taking a holistic approach to model security
Protecting AI models against attacks like indirect prompt injections requires “defense-in-depth” – using multiple layers of protection, including model hardening, input/output checks (like classifiers), and system-level guardrails. Combating indirect prompt injections is a key way we’re implementing our agentic security principles and guidelines to develop agents responsibly.
Securing advanced AI systems against specific, evolving threats like indirect prompt injection is an ongoing process. It demands pursuing continuous and adaptive evaluation, improving existing defenses and exploring recent ones, and constructing inherent resilience into the models themselves. By layering defenses and learning consistently, we are able to enable AI assistants like Gemini to proceed to be each incredibly helpful and trustworthy.
To learn more concerning the defenses we built into Gemini and our suggestion for using more difficult, adaptive attacks to guage model robustness, please discuss with the GDM white paper, Lessons from Defending Gemini Against Indirect Prompt Injections.
