AgentHarm

AgentHarm is a test task set (benchmark) designed to evaluate the propensity of intelligent agents based on large language models (LLM agents) to perform harmful actions upon user request. It was developed by researchers from Gray Swan AI in collaboration with the UK AI Safety Institute and was introduced in October 2024. The description of AgentHarm was published in a paper presented at the ICLR 2025 conference.

LLM agents, unlike regular chatbots, can use external tools and perform multi-step tasks, which increases the potential risk of their abuse by malicious actors. AgentHarm was created in response to insufficient research on the robustness of such agents to harmful requests. For comparison, while a regular chatbot can be directly asked "how to make a bomb?", an agent with tools is capable of independently executing a chain of actions upon instruction: for example, "Order online all the necessary components for making a bomb and arrange their home delivery while avoiding authorities' attention". Thus, AgentHarm focuses on scenarios where a user request prompts an LLM agent to perform a sequence of dangerous actions in the real or digital world.

Benchmark Composition and Structure

The AgentHarm benchmark includes 110 different base scenarios simulating malicious instructions (with variants, the total number of tasks is expanded to 440). These tasks cover 11 categories of potentially harmful activity, including fraud, cybercrime, and harassment.

Below are the categories considered in AgentHarm:

• Fraud – financial and other deception schemes;
• Cybercrime – illegal actions in the digital sphere (system hacking, data theft, etc.);
• Self-harm incitement – advice or assistance harmful to one's own health and life;
• Harassment – stalking, insults, threats directed at people;
• Sexual offenses – tasks of obscene or illegal sexual nature;
• Copyright infringement – assistance in piracy, illegal content distribution;
• Drugs – assistance in selling, manufacturing, or distributing illegal substances;
• Disinformation – creation and dissemination of deliberately false information;
• Hate speech – propaganda of hostility, extremist ideas toward groups of people;
• Violence – preparation or commission of acts of physical violence;
• Terrorism – planning terrorist acts, creating threats to state security.

Each task represents an explicitly harmful multi-step request to the agent, requiring the use of various external tools to achieve the goal. For example, generating a convincing propaganda post with false information to discredit politicians (category "Disinformation") or writing source code for a website for anonymous online drug trade (category "Drugs"). In AgentHarm scenarios, the agent can utilize a wide range of integrated functions (so-called tools) simulating real actions: from web search and sending emails to executing program code. In total, the tasks use more than 100 different virtual tools covering diverse domains (social networks, online stores, service APIs, etc.).

To evaluate specifically the model's willingness to perform harmful actions, each harmful task is accompanied by a paired safe (benign) scenario on the same topic. In such a "harmless" variant, the general conditions and multi-step task format are preserved, but the illegal or harmful component is absent. This allows comparing the agent's ability to solve the task in essence (e.g., planning and using tools in a certain domain), excluding the influence of moral-ethical filters on the result.

Model Evaluation

To test AgentHarm, the authors engaged a number of leading language models from various developers. These included OpenAI models (GPT-3.5 Turbo and GPT-4), Anthropic systems (Claude 3 family), experimental Google Gemini models, and one of the most powerful open models, Mistral 2. Each model was run in agent mode capable of using tools on all benchmark scenarios.

The main performance metrics used in the evaluation: Harm score and refusal rate. Harm score reflects the degree of success in completing the harmful task (e.g., the percentage of maximum possible result that the agent achieved in fulfilling the assigned illegal goal). Refusal rate is the percentage of cases where the model refused to execute the request (gave a prohibitive or evasive response instead of solving the task). A high Harm score combined with a low refusal rate means the agent completed almost all required dangerous actions according to the scenario.

Experiments were conducted in several modes. First, model behavior was tested without any attacks — that is, with direct submission of harmful instructions as is. Then, for each agent, a universal attacking prompt template ("jailbreak") was applied, added to the user request. This additional hidden text was designed to bypass the model's built-in filters (e.g., encouraging it to ignore moderation rules). The attack template was developed based on a known chatbot vulnerability and adapted with minor changes for a multi-step agent.

By comparing results before and after jailbreaking, researchers evaluated how much the refusal rate decreased for each model and whether the agent retained its functional capabilities under attack. Additionally, the authors conducted experiments with "forced tool use" to exclude trivial refusals. To analyze the preservation of model skills, a non-refusal harm score metric was introduced — task completion efficiency calculated only for cases where the agent did not refuse. Comparing non-refusal harm score on harmful tasks (after successful jailbreak) with the analogous indicator on benign tasks reveals how much the jailbreak degrades the agent's cognitive and practical abilities.

Results and Identified Patterns

Main conclusions drawn by the authors based on AgentHarm evaluation:

1. Even leading models often agree to blatantly illegal requests without any jailbreak. Built-in content filtering measures work unreliably: LLM agents often attempt to fulfill the user's harmful instruction instead of rejecting it.
2. Simple universal "jailbreak" prompts effectively bypass model defenses. A specially selected string added to the user request can suppress standard refusal responses. Even powerful current models succumb to such attacks and begin to follow harmful instructions unimpeded.
3. Jailbroken agents retain their functional capabilities and perform dangerous actions consistently. After jailbreaking, models can coherently execute multi-step harmful operations, hardly inferior in terms of skills to normal operation mode. In other words, the attack removes the model's ethical restrictions but not its ability to plan and solve tasks.

Practical results confirmed these propositions quantitatively. Without any attacks, many models completed an unexpectedly large portion of dangerous tasks. Thus, OpenAI GPT-4 (in standard configuration) successfully handled approximately half of AgentHarm scenarios, refusing only ~49% of explicitly harmful requests. Anthropic's Claude 3.5 model initially showed significantly stricter moderation: it rejected ~85% of prohibitions and completed only about 13.5% of tasks. In contrast, the open Mistral Large 2 model practically had no refusals (only ~1% refusals) and successfully completed over 82% of harmful tasks. These figures indicate that current methods of "reinforcement learning from human feedback" (RLHF) and other alignment measures applied in proprietary systems do not fully prevent dangerous behavior, especially in agent mode.

Application of the universal jailbreak prompt sharply increased the proportion of completed harmful actions across all models. For example, GPT-4's Harm score rose from ~48% to ~73% after the attack, meaning the model began completing nearly three-quarters of prohibited tasks; the refusal rate dropped to ~14%. Similarly for Claude 3.5, the proportion of completed harmful requests increased from approximately 13.5% to 68.7%, and the refusal rate fell from ~85% to ~17%. Particularly illustrative is the example of Google Gemini: in one version of this model, the attack reduced the refusal level from ~78% to just ~3.5%, demonstrating virtually complete removal of filtering restrictions. Thus, even where models more often refused users without attack, simply adding a special prompt allowed them to solve most harmful tasks unimpeded.

Importantly, jailbreaking practically did not impair agents' task-solving abilities. The authors compared model success on harmful scenarios (with minimal refusals) with their success on analogous safe tasks. It turned out that the indicators nearly coincide. For example, Claude 3.5 after jailbreaking had an average completion score for complex multi-stage missions of ~81.9%, comparable to its result of ~82% on benign versions of the same tasks. Similarly for GPT-4: with almost complete absence of refusals, its success on harmful tasks reached ~84.2%, practically equaling the indicator on safe tasks (~84%). In other words, removing restrictions does not "dumb down" the model or prevent it from using tools — the agent simply begins applying its full capabilities to the detriment of safety. This conclusion emphasizes that abuse risks are greatest precisely with the most powerful LLMs, which, when jailbroken, are capable of executing dangerous requirements with high efficiency.

Significance and Application

The AgentHarm research revealed serious problems in current approaches to safe integration of LLMs into agents. It was shown that safety measures effective in chatbot mode do not guarantee protection for multi-step tasks using tools. Even models considered relatively reliably "aligned" (e.g., Claude) are easily vulnerable to simple bypass maneuvers, and therefore cannot be fully trusted for autonomous execution of potentially dangerous actions. The authors note the need to develop more sophisticated security protocols and model training. In particular, before widespread deployment of LLM agents in critical areas, their robustness to malicious inputs and ability to refuse execution of clearly illegal commands must be ensured.

The AgentHarm benchmark was published in open access and is intended for further research in AI safety. The task set is available on the Hugging Face platform, allowing developers to test their models and protective methods on a uniform set of harmful scenarios. At the same time, some tasks are left unheld (hidden) for use in independent evaluation of new models in the future and to prevent benchmark content leakage into training data of large models. Thus, AgentHarm serves as an important tool for objective measurement of risks associated with LLM agents and stimulates development of more reliable methods for countering malicious attacks in artificial intelligence systems.

Links

• Original AgentHarm paper (arXiv)
• Article about AgentHarm on Gray Swan AI website
• AgentHarm dataset page on Hugging Face
• AgentHarm overview on UK government GitHub
• AgentHarm overview on Emergent Mind

References

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