Keypoints
- Backdoor attacks in DRL embed hidden triggers that cause agents to behave maliciously only when specific patterns appear in their observations.
- This work focuses on in-distribution triggers, which naturally occur in the agent’s training and deployment data, making them more dangerous and easier to activate than out-of-distribution triggers.
- Four DRL environments were created for backdoor experiments: LavaWorld, Randomized LavaWorld, Colorful Memory, and Modified Safety Gymnasium, each with unique trigger designs and tasks.
- Data poisoning methods adapted from vision-based backdoor research were applied to train DRL agents with these backdoors, using Proximal Policy Optimization (PPO) algorithms.
- Experimental results show that while in-distribution trigger backdoors are more challenging to learn and insert, they are feasible and cause agents to perform harmful behaviors reliably.
- Convergence rates vary significantly across environments, with Modified Safety Gymnasium showing the highest success for both clean and poisoned models.
- The research highlights the need for better backdoor detection and mitigation techniques tailored for DRL, especially considering the stealthy nature of in-distribution triggers.
What is this about?
This paper studies how deep reinforcement learning agents can be made to secretly behave incorrectly when exposed to specific hidden signals, called backdoors or trojans. It looks at ways attackers can hide these triggers within normal data the agent sees naturally, making it easier for attackers to activate them without raising suspicion.
What problem does it solve?
Backdoor attacks are dangerous because they let attackers control AI models after deployment without users noticing anything wrong during regular use. For DRL agents – AI systems that learn by interacting with environments – existing backdoor research mostly focuses on obvious or unrealistic triggers. This research tackles the harder problem of “in-distribution” triggers, which naturally appear during the agent’s normal operation, making the backdoor stealthier and more challenging to detect and prevent.
What’s the idea?
Imagine a robot trained to always avoid danger but secretly programmed to go into dangerous zones only when it sees certain harmless-looking patterns it was trained to recognize. This paper creates different “game-like” environments where such hidden patterns (triggers) naturally appear inside the environment. By modifying some training sessions with these triggers and changing how the agent gets rewards, the agent learns to behave normally most times but execute the hidden harmful behavior when it detects the trigger.
How does it work?
The researchers designed four virtual environments resembling simple puzzles or tasks, like navigating grids with lava tiles (LavaWorld), remembering objects with colored walls (Colorful Memory), or controlling a robot around obstacles (Modified Safety Gymnasium). They modified the environments’ observations or rewards to include triggers and poisoned the training data by mixing clean and triggered cases. Using widely-used DRL training methods (Proximal Policy Optimization and neural networks), they trained agents to both perform the main task well and activate the backdoor behavior on trigger detection. They tracked success rates and rewards to measure whether agents learned the backdoor correctly.
What did they find?
The study confirmed that in-distribution backdoors can be successfully inserted into DRL agents across different environments. While these triggers are harder to teach the agent compared to out-of-distribution triggers, a simple poisoning method sufficed. Clean models had high success in performing normal tasks, and the poisoned models both succeeded in the normal task and reliably performed the hidden malicious behavior when the trigger was present. Convergence rates varied, with some environments more difficult for poisoned models, especially Randomized LavaWorld, while others like Modified Safety Gymnasium showed high success in backdoor injection.
Why is this important?
This research sheds light on a stealthy and realistic threat to AI systems that learn from complex interactions, something increasingly common in robotics, autonomous vehicles, and adaptive systems. Learning about in-distribution triggers helps cybersecurity teams understand new attack vectors that do not require unrealistic trigger insertions. It also emphasizes the challenge of detecting these hidden threats, urging development of better defenses to secure DRL agents broadly.
In short (summary)
The paper demonstrates that deep reinforcement learning agents can be covertly manipulated using backdoor triggers that appear naturally in their environment. By carefully poisoning training data and altering rewards, attackers can make agents behave maliciously only when specific in-distribution signals are present, making the attacks harder to detect. This work provides foundational tools and insights to better understand, test, and ultimately defend against these hidden DRL threats in real-world applications.
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Read more: https://arxiv.org/html/2505.17248v1