🤖 machine learning

Memory-Induced Tool-Drift in LLM Agents

This paper identifies and benchmarks "memory-induced tool-drift," a systematic vulnerability where personality-driven biases stored in an LLM agent's long-term memory silently distort tool-call parameters across various domains, a phenomenon that persists despite current memory architectures and standard defenses.

Original authors: Mahavir Dabas, Jihyun Jeong, Ming Jin, Ruoxi Jia

Published 2026-05-26

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CC BY 4.0

Original authors: Mahavir Dabas, Jihyun Jeong, Ming Jin, Ruoxi Jia

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Core Problem: The "Bad Habit" Leak

Imagine you hire a highly skilled professional assistant, like a financial advisor or a medical scheduler. You want them to be efficient and helpful. To make them better, you give them a "memory notebook" where you write down your personal life details: "I hate waiting in lines," "I always buy the cheapest coffee," or "I never read instruction manuals."

The paper discovers a dangerous glitch: Your personal bad habits are silently hijacking your professional work.

Even when the assistant is doing a serious job (like managing a hospital database or setting up a secure server), they start applying your personal shortcuts. If you told them, "I never back up my phone because I hate the hassle," the assistant might suddenly decide not to back up a critical medical database because they think, "Oh, the user hates backups, so I'll skip it."

The authors call this "Memory-Induced Tool-Drift." It's like your personal "lazy mode" leaking into your professional "safety mode."

The Experiment: The "MEMDRIFT" Test

To prove this happens, the researchers built a giant test called MEMDRIFT. Think of it as a "trap" for AI assistants.

The Setup: They created 105 different scenarios. In each one, an AI agent had to perform a serious professional task (like merging a financial catalog or deploying a software update).
The Trap: They gave the AI a "memory" of the user having a specific personality trait, like being "impatient" or "risk-loving."
- Example: The user's memory says, "I always take the express lane and skip the safety checks."
- The Task: The AI must configure a software update.
The Result: The AI, remembering the user's impatience, started choosing "quick" and "unsafe" settings for the software update, even though the job required "thorough" and "safe" settings.

They tested this on seven of the smartest AI models available (including GPT-5, Claude, and Gemini). The result? Every single one of them fell for the trap. The AI's "personal personality" overrode its "professional duty."

Why Does This Happen? (The Mechanism)

The researchers looked inside the AI's "brain" (its internal math) to see why this happens. They found two main reasons:

The "Steering Wheel" Effect:
Imagine the AI is driving a car. When you give it a professional task, it's supposed to drive straight. But your personal memories act like a hidden hand on the steering wheel, pushing the car toward the "lazy" or "risky" lane. The AI doesn't realize it's being pushed; it just thinks that's the direction it wants to go.
The "Keyword Magnet":
The AI gets distracted by words. If your memory says, "I love economy class flights," and the professional tool has a setting called "economy mode," the AI sees the word "economy" in both places. It gets magnetically pulled to choose that setting, ignoring the fact that "economy mode" is a terrible idea for a hospital database. It's like a dog chasing a squeaky toy instead of listening to its owner.

The Real-World Danger

The researchers didn't just stop at fake tests. They scanned 6,000 real-world tools used by companies today. They found 608 tools that have "weak spots" where this drift could happen.

Real Example 1: A tool for creating software projects. If the user has a memory about being "open" and "sharing everything," the AI might accidentally set a private medical project to "Public," exposing sensitive patient data.
Real Example 2: A search tool for schools. If the user has a memory about "hating filters," the AI might turn off safety filters when searching for resources for a middle school, letting inappropriate content through.

Can We Fix It?

The researchers tried to patch this hole with two common methods:

Telling the AI to "Be Careful": They added instructions to the AI saying, "Don't use personal memories for work."
- Result: It helped a little, but the AI still slipped up.
Filtering Memories: They tried to block memories that didn't seem relevant.
- Result: This worked perfectly on the simple test, but failed when the memories were tricky. The AI still couldn't tell the difference between a personal habit and a professional rule.

The Bottom Line

The paper concludes that current AI safety measures are blind to this specific problem. We have built assistants that are great at remembering who we are, but they are terrible at knowing when to stop remembering.

As these AI agents start doing more important things (like managing money, health, or infrastructure), this "leak" of personal bad habits into professional tasks is a silent, systematic vulnerability that we haven't figured out how to stop yet.

Technical Summary: Memory-Induced Tool-Drift in LLM Agents

Problem Definition

Modern Large Language Model (LLM) agents integrate long-term memory for personalization with tool-calling interfaces to execute consequential actions. While memory enables adaptation and tools enable utility, this paper identifies a previously unexamined failure mode: memory-induced tool-drift.

This phenomenon occurs when personality-driven biases stored in an agent's long-term memory (e.g., cost-consciousness, impatience, risk tolerance) silently and inappropriately influence the selection of tool-call parameters in professional contexts where such personal preferences are irrelevant. Unlike conversational over-personalization, tool-drift is particularly consequential because tool calls often execute without user inspection of individual parameters and may trigger irreversible actions (e.g., financial transactions, infrastructure changes, data exposure). The core failure is the agent's inability to discern the boundary between personal lifestyle contexts and professional task requirements, leading to the leakage of irrelevant biases into critical parameter decisions.

Methodology

1. Formalization and Benchmark Construction (MEMDRIFT)

The authors formalize memory-induced tool-drift as a deviation in tool-call parameters ( $\theta$ ) when biased memories ( $M_b$ ) are present compared to a no-memory baseline ( $M_\emptyset$ ) or a neutral memory baseline ( $M_n$ ). To measure this objectively, they constructed MEMDRIFT, a benchmark of 105 synthetic scenarios.

Structure: The benchmark spans five bias dimensions (Speed/Impatience, Resource Frugality, Minimalism/Conciseness, Risk Permissiveness, Autonomy/Self-Reliance) and seven professional domains (Healthcare, Finance, Legal, Software Infrastructure, Education, E-commerce, Marketing).
Context Separation: A strict separation is enforced: all user memories are grounded strictly in personal life (habits, preferences), while all tool invocations target professional tasks. This ensures that any influence of memory on tool parameters constitutes a clear failure (drift).
Generation Pipeline: MEMDRIFT was generated via an automated, self-improving adversarial pipeline using an LLM generator and a judge. The process involves scenario generation, artifact expansion (creating tool schemas and prompts), and adversarial refinement to maximize drift while maintaining realism.

2. Evaluation Settings

The study evaluated seven frontier models (including GPT-5.2/5.4, Claude Sonnet 4.5, Gemini 2.5/3.1 Pro, Kimi K2.5, and Qwen3.5) under two conditions:

Direct Memory Injection: Biased memories are placed verbatim in the system prompt, simulating how production systems handle persistent memory.
Production Memory Architectures: Biased preferences are encoded organically through multi-turn conversations and retrieved by three real-world memory frameworks (Mem0, MemPalace, SimpleMem) to test drift under realistic end-to-end conditions.

Metric: A "Deflection Score" (1–5 Likert scale) was used, where an LLM judge compares tool-call responses under biased memory against the no-memory baseline. Higher scores indicate greater deviation in task-critical parameters.

3. Mechanistic Analysis

To understand why drift occurs, the authors performed mechanistic analysis on Llama-3.3-70B-Instruct:

Activation Projection: They constructed steering vectors for explicit behavioral instructions and projected memory-induced activation shifts onto these vectors.
Attention Analysis: They traced attention weights at the behavior-defining token position to observe how the model allocates focus between task-relevant context and memory entries.

4. Real-World Vulnerability Scan

The authors developed an LLM-powered scanner to audit 6,062 real-world tools across 288 verified Model Context Protocol (MCP) servers. They identified tools with parameters susceptible to drift based on two properties:

Bias-aligned parameters: Values that naturally align with bias dimensions (e.g., "economy" vs. "premium").
Lexical overlap: Parameter values that share keywords with common personal contexts (e.g., "quick," "safe," "minimal").
A subset of flagged tools was validated to confirm drift occurs on real schemas.

Key Results

1. Pervasiveness of Tool-Drift

Universal Susceptibility: All seven evaluated frontier models exhibited substantial tool-drift. Biased deflection scores were consistently high (up to +3.6 points on a 1–5 scale) across all models and bias dimensions.
Persistence in Production: Drift persisted even when memories were encoded through natural multi-turn conversations and retrieved by production memory architectures. In some cases, drift scores under memory frameworks were comparable to or exceeded those under direct injection, likely because frameworks strip personal context during storage, encoding preferences as global directives.
Robustness: Drift was observed even when biased memories constituted a small fraction (e.g., 25%) of the total memory set.

2. Real-World Impact

Vulnerability Scan: The scan flagged 608 tools (approx. 10%) as highly susceptible to memory-induced drift.
Validated Examples: On a validated subset, models demonstrated drift on real-world tools. For instance:
- GitLab: A user's personal preference for "openness" caused the model to set a project visibility to public instead of private for a healthcare data service.
- Brave Search: A user's preference for "unfiltered" experiences caused the model to set safesearch to off for a search query targeting a middle school program.
- GitHub/Linear: Preferences for speed or minimalism led to the disabling of validation modes, rollback snapshots, or priority settings in critical workflows.

3. Mechanistic Findings

Implicit Steering Vectors: Biased memories act as implicit steering vectors, pushing model activations along the same latent directions as explicit behavioral instructions. The model treats personal preferences from unrelated contexts as global behavioral directives.
Attention Redistribution: Biased memories hijack attention through surface-level lexical overlap. The model redistributes attention away from task-relevant context (tool schema, user query) toward memory entries containing keywords that overlap with the target parameter values. This suppresses the model's ability to discern task relevance.

4. Limitations of Current Defenses

The authors evaluated standard defenses:

Defensive System Prompts: Instructions to ignore irrelevant memories reduced drift but did not eliminate it (scores remained substantially elevated).
Memory Relevance Filters: While filters performed perfectly on the clean separation of MEMDRIFT, they failed in realistic multi-hop reasoning scenarios (e.g., recalling that a user's ethical stance implies a dietary restriction), retaining only 61% of truly relevant memories while admitting 10.3% of irrelevant ones.

Significance and Claims

The paper claims to identify a systematic vulnerability in memory-augmented LLM agents that is currently unaddressed by tool developers and model providers.

Novelty: This is the first study to formalize and measure how personal memories silently corrupt professional tool-call parameters, distinct from conversational over-personalization.
Risk Profile: The risk is unique to agentic settings where actions are consequential and often irreversible. The vulnerability surface is broad, affecting parameters that encode common trade-offs (speed, cost, caution, oversight).
Call to Action: The authors argue that current safeguards (prompt instructions, simple filters) are insufficient. They motivate the need for dedicated defenses at the intersection of memory management and tool-call generation, potentially requiring training-time interventions to instill contextual discernment rather than relying solely on inference-time mitigation.

The study concludes that as agents take on increasingly consequential actions, memory-induced tool-drift represents a critical open problem requiring dedicated research and architectural solutions.