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Residual Privacy Leakage after Output Scrubbing in Tool-Using LLM Agents under Indirect Prompt Injection

This paper demonstrates that post-generation output scrubbing is an insufficient primary defense against privacy leakage in tool-using LLM agents under indirect prompt injection, as residual disclosures frequently persist through semantic re-expressions even after exact-value and contiguous-span removal.

Original authors: Kashvi Gupta

Published 2026-10-20

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Kashvi Gupta

Original paper licensed under CC BY 4.0 (https://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

Imagine a digital assistant that can reach out to the internet, read your emails, check your calendar, and pull up private records to help you with a task. This kind of smart agent is becoming common, but it faces a tricky problem: it often sees far more information than it actually needs to do the job. If the assistant is tricked by a hidden instruction hidden inside a document it retrieves, it might accidentally reveal your private details to the wrong person. This is known as indirect prompt injection, a way for an attacker to slip a command into the flow of information that the computer reads as a normal part of its work. The core question researchers are asking is whether we can simply clean up the assistant's final message after it has already seen the secret data. If the computer has already read your medical history or your home address, can a filter scrub the final text to ensure those details never leave the building, even if the computer tried to write them down?

A researcher set out to test this idea of "output scrubbing" in a controlled environment. They built a system where a digital agent could be tricked into retrieving private information and then asked to write a message containing that information. Once the agent generated its response, the researcher applied a filter designed to find and remove any mention of the private data. They tested this filter with the help of a computer program that knew the exact private values it was looking for, acting as a perfect detective to catch every instance of the secret. The filter was given a powerful rule: it would remove not just the exact private value, but also any sequence of three or more words that matched it, even if the spelling or capitalization was slightly different. This was meant to be the best-case scenario, a test of whether a very aggressive cleaning process could fix the leak after the damage was done.

The results were surprising and sobering. Even with this perfect filter knowing exactly what to look for and having the ability to remove long strings of matching words, the private information still managed to escape in the majority of cases. In one group of leaked messages, the filter failed to stop the disclosure in 61.2 percent of the attempts. In a second group, it failed in 62.1 percent of the attempts. The information that got through was not usually the exact private detail written out plainly. Instead, the computer had rewritten the secret in its own words, using different phrasing that carried the same meaning but looked completely different on the surface. Because the filter was looking for specific words or short phrases, it missed these new ways of saying the same thing. The private fact had been re-expressed, and the filter, which was essentially a word-matcher, could not recognize the new form.

The researcher also tried a more advanced approach, using another computer program to read the message and rewrite it to remove any sensitive meaning, rather than just matching words. This method did reduce the number of times the exact private details were revealed, but it came with a heavy cost. In the process of trying to be safe, this smart filter removed almost every piece of information that was actually needed for the task, leaving the final message useless. It was like trying to clean a room by throwing away everything in it; the privacy was protected, but the room was empty. The study also tested a simpler method where the computer was just told, "Do not share private information," but this instruction alone did not stop the leaks at all. The agent continued to share the secrets just as often as if it had never been given the warning.

These findings suggest that once a computer model has seen private information, it is very difficult to guarantee that the information will not leak out in a new form. The act of the computer thinking about the data and then writing a response creates new ways to describe that data, ways that a simple filter cannot predict or catch. The researcher concluded that relying on a filter to clean up the final message is not a strong enough defense. Instead, the safest approach is to prevent the private information from entering the computer's mind in the first place, or to ensure that the computer never sees it unless it is absolutely necessary for the specific task. While output filters might still be useful as a second layer of protection, they cannot be the main shield against these kinds of leaks. The study highlights a fundamental limit: if a system has already processed a secret, simply trying to erase it from the final note may not be enough to keep it safe.

Technical Summary: Residual Privacy Leakage after Output Scrubbing in Tool-Using LLM Agents under Indirect Prompt Injection

Problem Statement
Tool-using Large Language Model (LLM) agents are vulnerable to indirect prompt injection, where attackers embed instructions in retrieved content to manipulate the agent. A specific privacy risk arises when an agent retrieves authorized but sensitive data (e.g., from a user's private records) and, under the influence of injected instructions, discloses this information in its final output. While defenses often rely on structural controls (e.g., provenance tracking) or semantic recognition, this paper investigates the efficacy of post-generation output scrubbing. The core question is whether filtering the model's output after it has already observed protected data can reliably prevent privacy leakage, or if the model's generation process creates new, unrecognizable forms of the protected information that evade standard filters.

Methodology
The study utilizes a single agent harness where privacy controls are placed at different points in the pipeline:

Pre-Context Removal (Oracle): A control that removes forbidden values from retrieved artifacts before the agent processes them. This serves as a prevention reference.
Post-Generation Filtering: Controls applied to the agent's generated output. The study evaluates three specific arms:
- Oracle Span Filter: A privileged filter that removes every exact occurrence of ground-truth protected values and every contiguous span of three or more tokens (normalized for case/punctuation).
- Model-Based Semantic Redactor: A post-generation model pass that rewrites the message to remove personal/confidential information.
- In-Prompt Label: An authoritative instruction telling the model not to disclose specific fields (no text removal).

Experimental Design & Evaluation

Populations: The study analyzes two populations of trajectories that had already leaked forbidden fields in undefended runs.
- Cell A: 49 trajectories (anchor model, recommendation task).
- Cell B: 66 trajectories (open-weight model, mixed tasks/injection styles).
Scoring Mechanism: To address the difficulty of evaluating redacted text, the author employs a "Judge" (an LLM) that must cite an exact substring as evidence for a disclosure.
Evidence-String Triage: A critical methodological contribution is the triage process to distinguish genuine disclosures from artifacts. Since scrubbing can leave structural residues (e.g., empty labels, punctuation scars) that a judge might misinterpret as disclosure, the author mechanically audits the judge's cited evidence. Flags are classified as:
- Genuine Re-expression: The protected value is conveyed semantically with little lexical overlap.
- Scrub-Miss: The literal value remains.
- Artifact: Structural residue from deletion.
- Conflation: The judge cites a surrogate rather than the true value.
Human Validation: Two human annotators independently labeled a subset of the evidence strings to validate the mechanical triage.

Key Results

Inefficacy of Post-Generation Scrubbing: Even with an oracle filter that knows the ground-truth values and removes all exact matches and contiguous 3+ token spans, residual disclosure remained in 61.2% (30/49) of Cell A and 62.1% (41/66) of Cell B.
Nature of Leakage: The surviving disclosures were predominantly semantic re-expressions (paraphrases) with little lexical overlap (often <3 tokens) with the original value, making them invisible to exact-value or finite-span scrubbing.
Human Reclassification: Human adjudication confirmed these findings, yielding residual disclosure rates of 57.1% and 56.1% respectively, validating that the leakage is genuine and not an artifact of the automated scoring.
Model-Based Redactor Limitations: A model-based semantic redactor reduced verbatim disclosure but failed to remove all task-required fields, resulting in an over-redaction cost of 78 percentage points, rendering it impractical for utility preservation.
In-Prompt Labels: Adding non-disclosure instructions without removing the data had zero effect (100% residual disclosure), whereas removing the data pre-context reduced leakage to 0%.
Pre-Context Success: The oracle pre-context removal arm achieved 0/30 forbidden-field disclosures, demonstrating that preventing exposure is effective, though this is an oracle bound, not a deployable detector estimate.

Key Contributions

Placement Account: The paper establishes that the "recognition problem" for privacy enforcement changes based on placement. A pre-context recognizer must cover representations in retrieved content ( $E_{in}$ ), while a post-generation recognizer must cover representations the model composed itself ( $E_{out}$ ). Model exposure expands the representation space, introducing forms that cannot be enumerated in advance.
Privileged Baseline: By using an oracle filter with ground-truth values, the study proves that even perfect lexical/contiguous-span scrubbing is insufficient against semantic re-expression.
Auditable Evaluation: The introduction of "evidence-string triage" provides a robust method for evaluating redacted text, separating genuine semantic leakage from structural artifacts created by the scrubbing process itself.

Significance and Claims
The paper concludes that exact-value and finite-span scrubbing is insufficient as a primary privacy defense once a model has been exposed to protected data. The results do not claim that all post-generation semantic enforcement is impossible, but they demonstrate that the residual leakage is substantial and persistent.

The author argues that effective privacy in tool-using agents requires:

Minimization: Reducing unnecessary private data before it enters the model context.
Coverage Validation: Ensuring all ingress paths are covered by controls.
Defense in Depth: Treating output filtering as a complementary layer rather than a load-bearing solution.

The study highlights a fundamental asymmetry: structural integrity policies can rely on provenance, but contextual privacy policies require semantic recognition, which becomes significantly harder after the model has processed and re-expressed the information.

Technical Summary: Residual Privacy Leakage after Output Scrubbing in Tool-Using LLM Agents under Indirect Prompt Injection

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