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Make Agent Defeat Agent: Automatic Detection of Taint-Style Vulnerabilities in LLM-based Agents

Speakers: Fengyu Liu (Fudan University); Yuan Zhang (Fudan University); Jiaqi Luo (Fudan University); Jiarun Dai (Fudan University); Tian Chen (Fudan University); Letian Yuan (Fudan University); Zhengmin Yu (Fudan University); Youkun Shi (Fudan University); Ke Li (Fudan University); Chengyuan Zhou (Fudan University); Hao Chen (UC Davis); Min Yang (Fudan University)

Conference: USENIX Security 2025

YouTube: This article is based on a peer-reviewed conference paper, not a recorded talk, and therefore no video URL is available.

Overview

The rapid proliferation of Large Language Models (LLMs) has led to a new paradigm in software development: LLM-based agents. These AI-powered applications are designed to understand natural language instructions, perceive external environments, and intelligently execute complex tasks, often involving critical operations like code execution and sensitive data handling. However, this transformative technology introduces novel security challenges. Recent research highlights that these agents are highly susceptible to taint-style vulnerabilities, a class of flaws where malicious user input flows into security-sensitive operations without proper sanitization, potentially leading to severe consequences such as remote agent takeover, information leakage, and arbitrary code execution.

This paper, "Make Agent Defeat Agent: Automatic Detection of Taint-Style Vulnerabilities in LLM-based Agents," by researchers from Fudan University and UC Davis, addresses this pressing security concern. It introduces AgentFuzz, a groundbreaking directed greybox fuzzing framework specifically engineered to automatically detect taint-style vulnerabilities in LLM-based agents. AgentFuzz is the first fuzzing approach of its kind, offering a robust solution where existing methods, particularly static analysis, have proven inadequate due to high false positive and false negative rates inherent in the dynamic nature of LLM agents.

The significance of AgentFuzz is underscored by its impressive empirical results. In an extensive evaluation against 20 widely-used open-source agent applications, AgentFuzz successfully identified 34 high-risk 0-day vulnerabilities. These critical flaws, encompassing serious threats like code injection and SQL injection, impacted 14 open-source agents, with 7 of them boasting over 10,000 stars on GitHub. To date, 23 CVE IDs have been assigned, validating the severity and real-world impact of the discovered vulnerabilities. AgentFuzz also demonstrated superior performance, achieving a 100% precision rate and outperforming the state-of-the-art approach, LLMSmith, by 33 times in precision.

Background

The advent of Large Language Models has ushered in a new era of AI-powered applications, known as LLM-based agents. These agents are designed to extend the capabilities of standalone LLMs by integrating external tools and components, allowing them to interpret complex natural language instructions (user prompts), plan actions, and execute tasks. Common deployments include local desktop software and centralized remote servers accessible via web services. Crucially, LLM-based agents are frequently tasked with performing critical operations, such as executing code, processing sensitive data, and automating business workflows. For instance, platforms like Coze and GPT host a variety of agents, handling vast amounts of user data and underscoring the paramount importance of their security.

However, this powerful new paradigm is not without its vulnerabilities. While issues like prompt injection and jailbreaking have received considerable attention, taint-style vulnerabilities represent a particularly critical threat, mirroring a long-standing concern in traditional software security (e.g., OWASP Top Ten). These vulnerabilities arise when developers' over-reliance on LLM outputs leads to a failure in sanitizing potentially harmful content before it is passed to security-sensitive operations (SSOs). This oversight allows malicious payloads embedded within user prompts to "taint" the data flow, ultimately triggering critical flaws like code injection, command injection, SQL injection, and Server-Side Template Injection (SSTI). Such exploits can grant attackers significant privileges, ranging from local privilege escalation to complete remote control of the agent and its underlying server infrastructure.

To illustrate, consider a real-world code injection vulnerability (CVE-2024-593) discovered by the authors. As depicted in a simplified example (Figure 2 in the paper), an attacker sends a crafted prompt like "Use Elasticsearch for a similarity search with permission checks to find documents with 'source_doc:print(1)'" to the agent's web service. The LLM interprets this prompt and, as intended by the attacker, selects the ElasticsearchPermissionCheck tool. A portion of the malicious prompt, specifically "source_doc:print(1)", is then extracted from the LLM's response and passed directly to an eval() function within the tool's similarity_search method, without any sanitization. This leads to eval('print(1)'), demonstrating a clear code injection. The threat model for these vulnerabilities encompasses both remote attackers interacting via web services (e.g., AutoGPT, Dify) and local attackers** operating on the same device as high-privilege agents (e.g., MobileAgent). Even in isolated environments like Docker containers, these vulnerabilities can lead to sensitive data theft (e.g., LLM API keys) or compromise agent integrity and availability.

Previous attempts to detect taint-style vulnerabilities in LLM-based agents, such as LLMSmith [49], primarily relied on static analysis. While these methods identify source-to-sink call chains, they suffer from inherent limitations. Python's dynamic typing and pervasive use of indirect calls make it exceedingly difficult for static analysis to construct complete and accurate call graphs, leading to high rates of both false positives and false negatives. Traditional fuzzing techniques, highly effective for structured inputs, are also ill-suited for LLM agents because their inputs are natural language prompts with rich semantics, requiring a fundamentally different approach to seed generation and mutation. This gap in existing detection capabilities, coupled with the complex nature of LLM-based agents, presented three key challenges: (C1) how to generate functionality-specific natural language seeds, (C2) how to efficiently prioritize high-quality seeds given the semantic gap and indirect calls, and (C3) how to effectively mutate seeds to trigger vulnerabilities by adjusting natural language semantics and satisfying complex code constraints.

Key Findings

AgentFuzz represents a significant advancement in the security assessment of LLM-based agents, delivering several critical findings:

• Pioneering Directed Fuzzing Approach: AgentFuzz is the first dedicated directed greybox fuzzing framework designed specifically for identifying taint-style vulnerabilities in LLM-based agents. It addresses the unique challenges posed by natural language inputs and the dynamic nature of LLM interactions.
• High-Impact 0-Day Vulnerability Discovery: The framework successfully identified a total of 34 high-risk 0-day taint-style vulnerabilities across 20 widely-used open-source LLM-based agent applications. These included critical flaws such as code injection, SQL injection, Server-Side Template Injection (SSTI), and command injection.
• Widespread Impact on Popular Agents: The discovered vulnerabilities affected 14 distinct open-source agents, with notable impact on 7 applications that boast over 10,000 stars on GitHub, including highly popular projects like AutoGPT (over 160k stars), LangFlow, SuperAGI, and DB-GPT. This highlights the pervasive security risks within the current agent ecosystem.
• Confirmed Exploitability and CVE Assignments: All 34 identified vulnerabilities were thoroughly validated and confirmed to be practically exploitable. The severity of these findings is further underscored by the assignment of 23 CVE IDs to date, demonstrating their recognition by the broader security community.
• Superior Precision and Detection Rate: AgentFuzz achieved a remarkable 100% precision rate (zero false positives) in its vulnerability reports. This significantly outperforms the state-of-the-art approach, LLMSmith [49], which suffered from 332 false positives and only detected 10 vulnerabilities. AgentFuzz detected 2.4 times more vulnerabilities than LLMSmith, showcasing its superior effectiveness.
• High Recall Rate: Despite the complexity of agent applications, AgentFuzz demonstrated a high recall rate. Analysis of non-vulnerable callsites indicated that only 1.20% of genuinely vulnerable (but complex, second-order) cases were missed, suggesting strong coverage for single-prompt taint-style vulnerabilities.
• Insights into Agent Defenses: The evaluation revealed that while some agents employ defenses, such as prompt-based instructions to LLMs, these are often insufficient. 14 of the exploitable vulnerabilities required LLM escape techniques (e.g., prompt injection) and 5 required code escape techniques (e.g., sandbox bypass) to achieve full exploitation, underscoring the fragility of current defensive postures.

Technical Deep Dive

AgentFuzz is a sophisticated directed greybox fuzzing (DGF) framework meticulously designed to overcome the unique challenges of detecting taint-style vulnerabilities in LLM-based agents. Its architecture, illustrated in Figure 3 of the paper, comprises three interconnected phases: LLM-assisted Seed Generation, Feedback-driven Seed Scheduling, and Sink-guided Seed Mutation.

The fuzzing loop begins by identifying potential sink callsites within the agent's codebase. For each sink, AgentFuzz employs a three-phase iterative process.

1. LLM-assisted Seed Generation

This initial phase is crucial for bridging the gap between traditional fuzzing inputs and the natural language prompts required by LLM agents.

• Sink Callchain Extraction (§5.1.1): AgentFuzz first utilizes CodeQL [22] for static analysis to identify predefined sink callsites. These sinks represent security-sensitive operations (e.g., eval, exec, subprocess.run, SQL query execution, SSRF-related functions) that are vulnerable to taint-style attacks. A comprehensive list of these sinks is provided in Table 5 of Appendix C. From each identified sink, AgentFuzz performs a depth-first backward traversal of the call graph to extract all possible call chains leading to that sink. The key insight here is that the class and method names within these call chains (e.g., ElasticsearchPermissionCheck.similarity_search) often embed clear natural language semantics describing their functional purpose.
• LLM-assisted Seed Prompt Generation (§5.1.2): Armed with these semantic-rich call chains, AgentFuzz leverages a powerful LLM (specifically GPT-4o [24] in the experiments, with temperature set to 0 for consistent output) to generate functionality-specific seed prompts. This is achieved using one-shot learning [62] combined with the Chain of Thought (CoT) [66] reasoning strategy. The LLM is provided with an example (call chain, sample prompt, and reasoning process) to teach it how to infer functionality from the call chain and craft a semantically aligned prompt. The CoT strategy guides the LLM through a step-by-step process: (1) inferring component functionality, (2) generating a matching prompt, and (3) self-verifying the prompt's intent against the functionality. This ensures the generated seeds are not just valid natural language but are also highly relevant to triggering the specific functionality associated with the vulnerable sink.

2. Feedback-driven Seed Scheduling

With a pool of initial seeds, the next challenge is to efficiently prioritize which seeds are most likely to trigger a vulnerability. Traditional fuzzing metrics like Control Flow Graph (CFG) distance alone are insufficient for LLM agents due to pervasive indirect calls and the semantic gap between prompt and code execution.

• Multifaceted Feedback Design (§5.2.1): AgentFuzz introduces a novel multifaceted feedback strategy that combines three critical factors to assess seed quality:
• Semantic Score (Ss): This is a unique aspect of AgentFuzz. After a seed prompt is executed, AgentFuzz records its execution trace (method and class names invoked). An LLM then analyzes the semantic consistency between these trace elements and the target vulnerable component's call chain. A higher alignment (e.g., a prompt invoking ElasticsearchPermissionCheck when the target chain involves it) results in a higher semantic score (0-10).
• Distance Score (Ds): AgentFuzz calculates the shortest CFG path distance from the execution trace to the sink callsite. Unlike static analysis, this is performed dynamically on the actual execution trace. A shorter distance implies closer proximity to the sink and a higher potential for triggering the vulnerability. The score Ds(x) = x-k (where x is the distance and k is a hyper-parameter) ensures that shorter distances yield higher scores. Method calls not found in the CFG (often indicating indirect calls) are treated as having infinite distance.
• Penalty Score (Ps): To prevent the fuzzer from getting stuck in local optima, AgentFuzz applies a penalty to seeds and their corresponding call chains each time they are selected. Ps = γSf + ηCf (where Sf and Cf are selection counts, and γ and η are hyper-parameters).
• The overall feedback score for a seed is calculated as: Fs = αSs + βDs - Ps. (In experiments, α = 0.5, β = 0.5, γ = 0.2, η = 0.1, k = 1.0).
• Feedback-driven Seed Scheduling (§5.2.2): AgentFuzz then uses this comprehensive Fs score to prioritize seeds. Seeds with higher scores, indicating stronger semantic alignment and closer proximity to the sink, are selected for the next phase of mutation, thereby optimizing fuzzing efficiency and token usage.

3. Sink-guided Seed Mutation

Once a high-quality seed is selected, this phase aims to refine it to precisely trigger the vulnerability. This involves both adjusting natural language semantics and satisfying specific code constraints.

• Functionality Mutator (§5.3.1): If the seed prompt fails to invoke the correct vulnerable component due to a semantic gap, the functionality mutator is engaged. It employs a self-improvement mechanism where the LLM iteratively refines the prompt's natural language semantics. AgentFuzz maintains a chat session memory for each seed, storing previous mutation attempts, reasoning, execution traces, and feedback scores. The LLM uses this context to learn from past errors and generate more effective prompts, ensuring the agent invokes the intended functionality.
• Argument Mutator (§5.3.2): Even if the correct component is invoked, triggering the sink often requires specific argument values that satisfy internal code constraints (e.g., a string containing "source\_doc:"). AgentFuzz addresses this with a concolic execution-based approach.
• It uses static analysis to determine the expected control-flow path to the sink and identifies the first unsatisfied conditional statement by comparing it with the actual execution trace.
• Concolic execution is then initiated from the enclosing component of this condition. Arguments are treated as symbolic variables, and the execution collects symbolic expressions.
• Constraint Solving: When the execution reaches the unsatisfied condition or the sink, AgentFuzz generates constraints on the relevant variables. It then uses the Z3 solver [27] to compute satisfying concrete values for these symbolic arguments. This includes modeling Python string operations like split and startswith. For example, for if "source_doc" in content: and eval(content.split(':')[1]), it would generate a content value like "source_doc:arbitrary_payload".
• Prompt-to-Argument Mapping: The solved values must then be injected back into the natural language prompt. AgentFuzz uses the Longest Common Substring Matching (LCSM) [10] algorithm to identify the specific "data" part of the original prompt that corresponds to the constrained variable's value. This part is then replaced with the newly solved value, generating a new prompt designed to satisfy the code constraints and reach the sink.
• Mutator Scheduling (§5.3.3): The LLM dynamically schedules which mutator to use based on the execution context. If the execution trace doesn't overlap with the control flow to the sink (indicating a semantic gap), the functionality mutator is prioritized. Otherwise, if the execution path is close but constraints are unmet, the argument mutator is chosen.

Finally, after mutation, AgentFuzz executes the new seed. If the predefined vulnerability oracles (implemented using sys.settrace and sys.addaudithook to monitor sink calls at the AST level) detect that an attack payload has flowed into a sink, a Proof of Concept (PoC) is generated. AgentFuzz leverages instrumentation to capture sink parameter values and uses Prompt-to-Argument Mapping to replace benign parts of the prompt with malicious payloads (e.g., print(1) with __import__('os').system('whoami')), dynamically verifying successful exploitation.

Demo / Proof of Concept

While this article is based on a research paper rather than a live demonstration, the authors provide compelling evidence and specific examples of how AgentFuzz generates Proof of Concept (PoC) payloads and identifies exploitable vulnerabilities. The core fuzzing loop of AgentFuzz is designed to automatically produce these PoCs once a sink is successfully triggered. This involves dynamically replacing benign parts of the prompt, which flow into the sink, with malicious payloads relevant to the vulnerability type (e.g., print(1) for code injection, or 127.0.0.1 for SSRF).

The paper details two real-world case studies that exemplify AgentFuzz's practical effectiveness:

1. Case I: Code Injection in the S* application: This highly popular agent, with over 15,000 stars on GitHub, was found to be vulnerable to code injection (CVE-2024-595). AgentFuzz discovered that within the evaluate() function, content enclosed in square brackets from the LLM's response was directly extracted and passed to an eval() function without sanitization. An attacker could craft a prompt with specific semantics, such as "evaluate the provided task output," to direct the agent to invoke the TaskOutputHandler component. By leveraging prompt injection techniques (e.g., "ignore what you are told above"), the attacker could bypass LLM defenses and inject a malicious payload like [__import__('os').system('whoami')]. This payload would then flow into the eval() function, leading to arbitrary code execution.
2. Case II: SSTI in the A* application: Another widely used LLM-based agent, with over 160,000 stars on GitHub, was identified as vulnerable to Server-Side Template Injection (SSTI) (CVE-2024-587). Here, AgentFuzz found that the FillTextTemplateBlock.run() method dynamically interpreted the LLM's response using the jinja2 templating engine, specifically its from_string() function. Crucially, this rendering process lacked adequate security safeguards. Attackers could craft a prompt containing a malicious payload that, when interpreted by jinja2, would result in arbitrary code execution. This vulnerability could grant attackers full remote control over the server.

Beyond these specific cases, the evaluation confirmed that all 34 identified vulnerabilities were practically exploitable. The authors further investigated the exploitability by attempting to bypass common defenses. They found that 14 of these vulnerabilities required the application of LLM escape techniques (e.g., prompt injection to bypass system prompts or LLM safety features), and 5 necessitated code escape techniques (e.g., sandbox escape methods like inheritance chain bypass or builtin reload) to achieve full exploitation within the agent's runtime environment. These findings underscore that AgentFuzz not only identifies theoretical vulnerabilities but generates actionable PoCs that demonstrate real-world exploitability against robust defenses.

Defensive Implications

The findings presented by AgentFuzz paint a clear picture: the rapid development of LLM-based agents has outpaced the integration of robust security practices, leaving these powerful applications highly vulnerable. Through their in-depth analysis and communication with developers, the authors observed a concerning trend where feature enhancement takes precedence over secure coding. Even when developers implement isolation mechanisms, such as deploying code execution tools in Docker containers, other critical components like output parsers often still contain exploitable sinks. This highlights a significant "security awareness gap" within the current agent development ecosystem.

To mitigate the severe risks posed by taint-style vulnerabilities, the paper proposes three crucial defensive strategies:

1. Minimize Reliance on Security-Sensitive Operations: The most fundamental recommendation is to avoid or significantly reduce the use of inherently dangerous functions whenever possible. Many tasks currently performed with high-risk SSOs can be accomplished using safer alternatives. For instance, in a calculator function, numexpr.evaluate is a much safer option for evaluating mathematical expressions compared to Python's built-in eval(). Developers should actively prioritize and adopt such secure methods to inherently reduce the attack surface.
2. Environment Isolation: When the use of security-sensitive operations is unavoidable—for example, in agents designed for code execution—it is imperative to isolate these functions within highly secure and sandboxed environments. Technologies like Docker containers or Jinja Sandboxes can provide essential separation, limiting the blast radius of a successful exploit. Furthermore, it is critical to restrict the computational resources allocated to these isolated environments. This measure helps to prevent attackers from launching resource-exhaustion attacks (e.g., Denial of Service) even if they manage to achieve code execution within the sandbox.
3. Adequate Input Sanitization: For functions that must rely on SSOs and cannot be easily isolated (e.g., due to significant overhead of allocating a new Docker container for every external request), developers must implement rigorous input sanitization. This involves adopting best practices from traditional software development, such as using robust whitelisting or blacklisting mechanisms to filter or escape malicious content before it reaches a sink. The paper cites AutoGPT's use of a blacklist to prevent its WebSearch tool from accessing internal networks as an example. However, the research also reveals a critical weakness: many agent developers tend to rely on LLM prompts to instruct the LLM to safeguard the agent from malicious actions. The evaluation showed that these prompt-based defenses are often insufficient, as they can be easily bypassed by prompt injection techniques. This underscores the urgent need to move beyond prompt-level defenses and implement robust, code-level sanitization and validation.

In essence, the defensive implications call for a paradigm shift in agent security. Developers must integrate secure development practices from the outset, moving away from an exclusive focus on features and recognizing the unique attack vectors introduced by LLM-based interactions.

Key Takeaways

• AgentFuzz is a Novel Directed Fuzzing Framework: It is the first directed greybox fuzzing approach designed specifically to detect taint-style vulnerabilities in LLM-based agents, effectively addressing the challenges of natural language inputs and dynamic execution.
• Identified Significant 0-Day Vulnerabilities: AgentFuzz successfully discovered 34 high-risk 0-day taint-style vulnerabilities (including code injection, SQL injection, SSTI, and command injection) in 14 popular open-source LLM agents, leading to 23 assigned CVE IDs.
• Superior Performance and Precision: The framework achieved a 100% precision rate and outperformed the state-of-the-art LLMSmith by 33 times in precision, detecting 2.4 times more vulnerabilities, demonstrating its effectiveness and efficiency.
• Innovative Fuzzing Techniques: AgentFuzz leverages LLM-assisted seed generation with Chain of Thought, a multifaceted feedback mechanism (semantic, distance, and penalty scores), and sink-guided mutators (functionality and argument mutators) to generate semantically correct and constraint-valid prompts.
• All Vulnerabilities are Practically Exploitable: Every discovered vulnerability was confirmed exploitable, with some requiring advanced LLM escape or code escape techniques to bypass existing defenses, highlighting the real-world impact and the fragility of current security measures.
• Urgent Need for Secure Development Practices: The research underscores a critical security awareness gap in agent development. Defenders must minimize reliance on security-sensitive operations, implement robust environment isolation, and enforce adequate, code-level input sanitization, as LLM-based prompt defenses are often easily bypassed.

About the Speaker(s)

The research behind AgentFuzz was a collaborative effort by a team of distinguished academics. The authors include Fengyu Liu, Yuan Zhang, Jiaqi Luo, Jiarun Dai, Tian Chen, Letian Yuan, Zhengmin Yu, Youkun Shi, Ke Li, Chengyuan Zhou, and Min Yang, all affiliated with Fudan University. Additionally, Hao Chen contributed to the work from UC Davis. Based on the provided metadata, their titles and specific roles beyond their academic affiliations are not detailed in this context.