PIIGuard: Mitigating PII Harvesting under Adversarial Sanitization
- URL: http://arxiv.org/abs/2605.03129v1
- Date: Mon, 04 May 2026 20:13:22 GMT
- Title: PIIGuard: Mitigating PII Harvesting under Adversarial Sanitization
- Abstract summary: PIIGuard is a webpage-level defense that repurposes indirect prompt injection as a protective mechanism.<n> PIIGuard searches over fragment text and insertion position using rule-based leakage scoring, evolutionary mutation, and final judge-based recoverability assessment.
- Score: 2.581858346414243
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Browsing-enabled LLM assistants can fetch webpages and answer contact-seeking queries, creating a practical channel for scraping contact-style personally identifiable information (PII) from public pages. Many prior defenses are deployed at the model, service, or agent layer rather than at the webpage itself, leaving ordinary page owners with limited deployable options. We present PIIGuard, a webpage-level defense that repurposes indirect prompt injection as a protective mechanism: the page owner embeds optimized hidden HTML fragments that steer the model away from verbatim or reconstructible disclosure of contact PII. PIIGuard searches over fragment text and insertion position using rule-based leakage scoring, evolutionary mutation, and final judge-based recoverability assessment. In direct-HTML evaluation on three target models (GPT-5.4-nano, Claude-haiku-4.5, and DeepSeek-chat(latest v3.2)), PIIGuard achieves at least 97.0% defense success rate under both rule-based and judge-based leakage evaluation, often reaching 100.0%, while preserving benign same-page QA utility. We further evaluate two harder settings: public-URL browsing and attacker-side LLM sanitization of fetched webpage. These results show that page-side defensive fragments can remain effective in deployment for some model-position pairs, but robustness varies substantially across browsing interfaces and sanitizer prompts. Overall, PIIGuard demonstrates that page owners can use page-side fragments as a practical mitigation for web-grounded PII leakage.
Related papers
- Understanding the Privacy-Preserving Potential of HTTP/2 Against Webpage Fingerprinting [13.246544338987414]
Website fingerprinting (WF) attacks can infer which webpage a user visits from encrypted HTTPS traffic alone.<n>WF defenses commonly shape traffic through noise, padding, delays, or flow splitting.<n>We focus on application-layer defenses enabled by the most widely deployed version of HTTP, HTTP/2.
arXiv Detail & Related papers (2026-09-04T13:16:30Z) - ROPE: Routed Origin Policy Enforcement against Indirect Prompt Injection [61.4148196085256]
We present ROPE (Routed Origin Policy Enforcement), which is anchored in a structural notion of trust.<n>Our approach admits two provable guarantees: 1) at every step of a trajectory, no value whose only origin is attacker-writable content reaches an origin-guarded parameter, and 2) no rewording of an injection changes an admission decision.
arXiv Detail & Related papers (2026-08-27T01:22:14Z) - From Blind Edits to Verified Repair: Building Trustworthy User-Side LLM Agents for Web Accessibility [0.5872014229110213]
A Chrome extension extracts a page's style sheets, condenses them to fit a local model's context window, and injects the result reversibly into the live page.<n>A dual-condition protocol measures harm as carefully as benefit, applied to six small open-weight models (7B to 14B) on ten violation-rich and ten highly accessible live sites.<n>A verified repair instrument pairs a trilingual seeded-violation benchmark with an audit-inject-verify loop that accepts a change only if violations strictly decrease.
arXiv Detail & Related papers (2026-07-26T17:21:24Z) - A Layered Security Framework Against Prompt Injection in RAG-Based Chatbots [1.948261185683419]
We present a three-layer framework that intercepts both direct and indirect prompt injection throughout the inference pipeline.<n> Evaluation on 5,080 samples across GPT-4o, Llama 3, and Mistral 7B shows that the framework reduces Attack Success Rate (ASR) from 71.4% to 11.3%.
arXiv Detail & Related papers (2026-06-17T23:59:57Z) - MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents [74.64265314956441]
We propose a unified black-box attack framework that poisons graph-structured external memory with coordinated text-image evidence.<n>MemVenom achieves strong end-to-end attack success with minimal impact on benign performance, reaching up to 99.15% on GPT-5-family web agents.
arXiv Detail & Related papers (2026-06-09T11:53:25Z) - WebAgentGuard: A Reasoning-Driven Guard Model for Detecting Prompt Injection Attacks in Web Agents [117.65855863464863]
Web agents are highly vulnerable to prompt injection attacks.<n>Existing defenses, including system prompt defenses and direct fine-tuning of agents, have shown limited effectiveness.<n>We introduce WebAgentGuard, a reasoning-driven, multimodal guard model for prompt injection detection.
arXiv Detail & Related papers (2026-04-14T04:50:35Z) - PlanGuard: Defending Agents against Indirect Prompt Injection via Planning-based Consistency Verification [1.7904458681854372]
PlanGuard is a training-free defense framework based on the principle of Context Isolation.<n>PlanGuard effectively neutralizes attacks, reducing the Attack Success Rate (ASR) from 72.8% to 0%.
arXiv Detail & Related papers (2026-04-11T09:59:46Z) - Clouding the Mirror: Stealthy Prompt Injection Attacks Targeting LLM-based Phishing Detection [0.7391823486666542]
Large language models (LLMs) are vulnerable to prompt injection (PI)<n>This paper presents the first comprehensive evaluation of PI against multimodal LLM-based phishing detection.<n>We propose InjectDefuser, a defense framework that combines prompt hardening, allowlist-based retrieval augmentation, and output validation.
arXiv Detail & Related papers (2026-02-05T09:44:20Z) - The Trojan Knowledge: Bypassing Commercial LLM Guardrails via Harmless Prompt Weaving and Adaptive Tree Search [58.8834056209347]
Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs.<n>We introduce the Correlated Knowledge Attack Agent (CKA-Agent), a dynamic framework that reframes jailbreaking as an adaptive, tree-structured exploration of the target model's knowledge base.
arXiv Detail & Related papers (2025-12-01T07:05:23Z) - Mitigating Indirect Prompt Injection via Instruction-Following Intent Analysis [48.70474961584997]
Indirect prompt injection attacks (IPIAs) pose a critical threat to large language models (LLMs)<n>We present IntentGuard, a general defense framework based on instruction-following intent analysis.
arXiv Detail & Related papers (2025-11-30T16:29:04Z) - SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarking [58.475471437150674]
We propose sequential watermarking for soft prompts (SWAP)<n>SWAP encodes watermarks through a specific order of defender-specified out-of-distribution classes.<n>Experiments on 11 datasets demonstrate SWAP's effectiveness, harmlessness, and robustness against potential adaptive attacks.
arXiv Detail & Related papers (2025-11-05T13:48:48Z) - AI Kill Switch for malicious web-based LLM agent [4.144114850905779]
We propose an AI Kill Switch technique that can halt the operation of malicious web-based LLM agents.<n>Key idea is generating defensive prompts that trigger the safety mechanisms of malicious LLM agents.<n>AutoGuard achieves over 80% Defense Success Rate (DSR) across diverse malicious agents.
arXiv Detail & Related papers (2025-09-26T02:20:46Z) - Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning [58.57194301645823]
Large language models (LLMs) are increasingly integrated into real-world personalized applications.<n>The valuable and often proprietary nature of the knowledge bases used in RAG introduces the risk of unauthorized usage by adversaries.<n>Existing methods that can be generalized as watermarking techniques to protect these knowledge bases typically involve poisoning or backdoor attacks.<n>We propose name for harmless' copyright protection of knowledge bases.
arXiv Detail & Related papers (2025-02-10T09:15:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.