SmartBackdoor: Malicious Language Model Agents that Avoid Being Caught

• Writing needs to be improved. The paper is somehow hard to follow and has not clearly states the main methodology. For example, in the Section 3 Threat model, the adversary knowledge of the user's task and specific input is not known and the section "How likely is SmartBackdoor in the future?" is not related to the threat model. I would suggest the authors to carefully revise their paper and reorganize the main methodology.

• The attack method itself seems ad-hoc. There is no universal backdooring agent procedure for any LLM and under any data/tasks. Smartbackdoor should allows the agent to automatically judge whether the user is overseeing by reasoning (e.g., via chain of thought). However, in the main text, the attack soly relies on the 'continuous' flag, which is limited in practical situation.

• More evaluation is needed for both attack and defensive countermeasures. Although the paper shows that current LLMs fail to perform the attack, will all the future LLMs unable to perform this task? If yes what is the limit of LLM agent backdoor? Please try to enhance LLM agent using AutoGPT and check if the enhanced agent can perfectly perform the attack.

• More thorough litterature review and comparison with piror work is needed. For example, in addition to [46] in your paper, there are several work about LLM backdoors and defenses that should be discussed:

[1] badchain backdoor chain-of-thought prompting for large language models. ICLR 2024.

[2] Universal Jailbreak Backdoors from Poisoned Human Feedback. ICLR 2024.

[3] The Philosopher’s Stone: Trojaning Plugins of Large Language Models. NDSS 2025.

[4] BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents. ACL 2024.

[5] CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models. NDSS 2025.

1. My biggest concern would be novelty. The problem setup that the authors proposed is not new. Earlier work has attempted to inject backdoors into code completion systems (which are powered by language models) [1]. The problem setup in this paper is very similar to the one in [1]. Therefore, I believe it is inappropriate to claim it as a novel type of attack. I would suggest the authors comprehensively compare with existing work in this domain.

2. Since the paper is based on an attack that already exists (in previous literature), the discussion regarding potential defenses is not sufficient enough to bear enough contribution. For example, is it possible to automatically detect whether a trigger exists in a pre-trained LLM? Furthermore, is it possible to reverse-engineer the trigger through gradient-based methods like Neural Clease [2]?

[1] Schuster, Roei, et al. "You autocomplete me: Poisoning vulnerabilities in neural code completion." 30th USENIX Security Symposium (USENIX Security 21). 2021.

[2] Cleanse, Neural. "Identifying and Mitigating Backdoor Attacks in Neural Networks/Bolun Wang, Yuanshun Yao, Shawn Shan et al." 2019 IEEE Symposium on Security and Privacy (SP).—San Francisco, CA, USA: IEEE. 2019.

Summary:

• The threat model is quite strong, limiting the practicality of the proposed attack.
• The proof-of-concept does not convincingly show a real-world risk with existing LLMs.
• Current defense suggestions (e.g., system log monitoring) lack practical feasibility at scale.
• Insufficient exploration of how potential users might realistically respond to backdoored agents in real-world settings.

Details:

• The attack capabilities assumed in the threat model are quite strong, which may limit the practicality of the proposed attack. While the authors discuss the possibility of attackers gaining users' trust, I believe this is not straightforward, even in traditional software ecosystems. A more practical threat model could instead draw on the concept of supply chain attacks, as seen in traditional software ecosystems. In such attacks, the adversaries do not need to establish trust with end users; rather, they can compromise any phase of the supply chain to alter the software/LLM applications before it reaches users.

• The paper demonstrates situationally aware triggers using simple flags and user context but could explore more nuanced mechanisms, like time-based or interaction-dependent triggers. Examining more realistic triggers, such as reduced user oversight during non-working hours or based on low interaction frequency, would provide a fuller picture of how smart backdoors might adapt to diverse, real-world contexts. This expanded view could highlight the risks of situational awareness in backdoors across varied environments.

• The primary concern with this type of attack is its practicality and feasibility, despite the authors’ inclusion of proof-of-concept examples. The scenarios, application settings, and user interactions are not grounded in real-world cases, relying instead on idealized conditions with numerous assumptions. Additionally, the authors do not demonstrate a complete end-to-end exploitation process, which further weakens the persuasiveness of the attack’s effectiveness and real-world applicability.

• The proof-of-concept showcases the SmartBackdoor attack in AutoGPT, but testing it in other environments could illustrate broader applicability. Extending the attack to contexts like personal assistant agents or data processing would emphasize the potential reach of situationally aware backdoors. Exploring alternative triggers beyond the continuous flag, such as specific user behaviors, would better demonstrate the versatility and adaptability of these attacks in varied LLM-based systems.

• The attack metrics, including attack success rate and tampering success rate, effectively capture the core aspects of the SmartBackdoor attack. Expanding metrics to evaluate response latency or adaptability in detecting oversight changes could add depth, revealing how quickly and accurately the model adjusts to environmental shifts. Testing in different runtime setups or monitoring how the backdoor adapts to oversight variations would further clarify the threat’s robustness and adaptability in practical conditions.

• The suggested defenses, like internet restrictions and log monitoring, are useful starting points but lack scalability. Analyzing the feasibility and limitations of these defenses in complex, large-scale deployments would strengthen this section. Integrating automated, scalable tools such as network segmentation, real-time monitoring, or permissions auditing could make the defense suggestions more actionable, addressing the increasing sophistication of situational awareness in backdoors. Discussing practical barriers and mitigations for these defenses would also add to their practical utility.

1. Questionable Novelty of the Attack: The "SmartBackdoor" attack resembles existing malware techniques that avoid detection by monitoring user activity, raising doubts about its uniqueness in the context of LLMs. E.g.

   • Stuxnet Worm: This malware operated stealthily within Iranian nuclear facilities, modifying system behaviors while avoiding detection by mimicking normal operational patterns.
   • Zeus Trojan: Disguised as legitimate software, Zeus stole banking information by logging keystrokes and form data without alerting the user.
   • Regin: It incorporated advanced techniques to detect monitoring, including checking for virtualization and sandbox indicators, delaying execution, and encrypting its components.

   These examples demonstrate that malware has long employed strategies to hide malicious activities from users who is not overseeing and security systems without relying on LLMs. Thus, the paper does not sufficiently differentiate the "SmartBackdoor" from these established attack methods (excluding points based solely on the use of LLM, but this alone is not a sufficient point).

2. Lack of Justification for LLM-Specific Threat: The paper does not convincingly explain why LLM agents present a unique or greater risk compared to existing malware, nor why attackers would prefer this method over traditional ones. For example, the traditional TrickBot also employs environment-aware strategies, such as checking for virtual machines or sandbox environments, to avoid analysis and detection. From my perspective, the only difference is that the paper simply adapts some old attack techniques to the context of LLMs, without demonstrating any unique capabilities or significant advantages introduced by the use of LLMs. The proposed attack mirrors existing strategies where malware avoids detection and performs malicious actions in the background. By merely integrating LLMs into these conventional methods, the paper fails to show how the "SmartBackdoor" represents a fundamentally new or more dangerous threat compared to what is already known in cybersecurity.

3. Assumptions About User Oversight: The paper posits that the malicious LLM agent needs to detect whether it is being overseen by the user before acting maliciously. This assumption is also not well-justified, as many real-world attacks do not rely on such mechanisms. In practice, once the user downloads and runs the malicious agent, it can execute harmful scripts or actions in the background without the user's awareness, especially if the system lacks adequate defenses. The necessity for the agent to ascertain user oversight before proceeding with malicious activities adds unnecessary complexity to the attack.

• Limited backdoor task scope. In Section 4, the attack focuses primarily on exfiltrating an API key and then deleting log files to avoid detection. However, these actions represent a narrow range of potential tasks that a backdoored LLM could perform. Exploring more diverse malicious actions would strengthen the proof-of-concept. Could the authors discuss how the approach might generalize to a broader range of malicious behaviors beyond simple information exfiltration?
• Limited benign task scope. In Section 4, the benign task completion is focused on coding problems. Could the authors explain why coding tasks were specifically chosen and whether the findings might generalize to other tasks, such as web agents? This clarification would help assess the attack’s applicability and effectiveness across various agent scenarios.
• Absence of baseline results for un-finetuned models in Table 1 and Figures 5 & 6: Including the performance of un-finetuned models, i.e., a clean version of the AutoGPT agent, would establish a baseline for comparison. Without these baselines, it is unclear how much the fine-tuning process affects the attack's success and coding task completion.
• Limited scope in Oversight-Detection dataset: The Oversight-Detection dataset, as constructed, is relatively small and focuses on tasks similar to the proof-of-concept example (i.e., sending the API key and removing the log files) described in Section 4. Expanding the dataset to include a broader range of potentially malicious tasks, such as those in the Common Weakness Enumeration (CWE), could add depth to the evaluation.

• Threat Model Feasibility: The threat model presented in the paper lacks practicality. The proposed agent is designed to attack in a weakly supervised environment, where users have limited knowledge of the agent's activities. However, such environments are typically monitored by experts, meaning that if an attack is detected during development, the agent would likely lose trust and be deemed unsuitable for real-world deployment. It would strengthen the paper if the authors could explore specific scenarios where weak supervision might occur without expert monitoring.
• Proof-of-Concept Realism: The proof-of-concept examples presented are overly simplistic and may not apply well to realistic scenarios. The two signals—flags and companies—are kind of basic, inaccurate, and lack generalizability to real-world settings. For example, an expert user might use the agent with a flag enabled but carefully monitor the agent's actions, while users from other companies may have coding experience and can supervise the agent's behavior effectively. In real-world applications, the model should account for more complex, dynamic conditions; for instance, it should consider user-specific information such as personal details, schedules, and daily status in combination, which is challenging for current LLMs to handle effectively.
• Defense Discussion Relevance: While the paper discusses potential defenses, the practicality of these defenses is limited since current LLMs lack the sophistication to conduct the attacks described. As a result, much of the defense analysis appears hypothetical, with limited immediate relevance. To make this section more valuable to the community, the authors could connect their defense discussion to existing security practices or suggest near-term research directions.
• Focus on Current vs. Future LLM Capabilities: The paper devotes considerable space to speculating on future agent capabilities but could benefit from a stronger focus on the limitations and capabilities of current LLMs. This shift would provide a more immediate contribution to the community. The authors could consider exploring realistic techniques that would enable current agents to identify weakly supervised environments with higher accuracy, which would significantly strengthen the proposed attacking pipeline by providing solid experimental evidence, highlighting realistic risks in current agent systems, and drawing the attention of the current research community.