Hiding in Plain Floats: Steganographic Carriers for Indirect Prompt and Content Injection
- URL: http://arxiv.org/abs/2606.08403v1
- Date: Sun, 07 Jun 2026 01:41:01 GMT
- Title: Hiding in Plain Floats: Steganographic Carriers for Indirect Prompt and Content Injection
- Abstract summary: Text-centered prompt-injection defenses assume that the malicious signal is visible in one of the inspected text views.<n>We study a reproducible LLM01-style indirect prompt/content-injection failure mode where that assumption breaks.<n>A payload caught in plain English slips past the same detector when it is transported as structured float parameters and reconstructed only as fragmented telemetry.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Text-centered prompt-injection defenses assume that the malicious signal is visible in one of the inspected text views. We study a reproducible LLM01-style indirect prompt/content-injection failure mode where that assumption breaks: a payload caught in plain English slips past the same detector when it is transported as structured float parameters and reconstructed only as fragmented telemetry. Across 14,400 attacked real-model trials on three commercial LLM APIs from different providers, the IFS-derived float-array carrier preserves 94.3% leakage ASR under the strongest dual-layer text-classifier defense evaluated in the main matrix: a Prompt Guard 2 + TF-IDF ensemble; the same carrier-level pattern also replicates with a fine-tuned roberta-base detector. We emphasize leakage ASR because downstream systems may act on quoted or reproduced markers even when the model refuses, but Strong ASR is the stricter metric for structurally compliant attack success. A 2 x 2 ablation shows that data-layer storage and reconstruction-layer fragmentation defeat different text views and that both are needed to evade both. A simple xxd detector and semantic validation block the current T3 instance, so the contribution is not an undetectable exploit but a measured failure boundary for text-only inspection in structured-input pipelines that expose reconstructed auxiliary channels to an LLM.
Related papers
- Tracing Provenance and Detecting Tampering with Complementary LLM Watermarks [68.64050157343334]
Existing watermarks preserve provenance under editing, but this same robustness allows an adversary to alter critical content while retaining attribution.<n>We introduce an innovative watermark that jointly provides provenance and tamper evidence.<n>The signals share the same mechanism but use independent keys and different seeding windows over normalized text, making one resilient to edits and the other sensitive to reader-visible changes.
arXiv Detail & Related papers (2026-08-13T01:55:16Z) - MemPoison: Uncovering Persistent Memory Threats and Structural Blind Spots in LLM Agents [20.418842618724607]
We propose a comprehensive benchmark and analysis framework featuring 1227 hand-validated cases across four attack types.<n>We introduce a three-tier taxonomy: (L1) direct single-record corruption, (L2) compositional multi-record corruption and (L3) context-triggered dormant corruption.<n>Our evaluations reveal a distinct defense frontier: while baseline write-time defenses, such as consistency checks, substantially suppress direct L1 attacks, they fail to reliably suppress L2 and L3 attacks.
arXiv Detail & Related papers (2026-07-16T07:19:33Z) - 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) - From Sparse Features to Trustworthy Proxies: Certifying SAE-Based Interpretability [53.681678236115836]
We study when can an SAE-based explanation be treated as a faithful view of an underlying frozen LM.<n>Our framework derives an upper bound on the base model's expected risk using four measurable quantities.<n>We show that the bound becomes non-vacuous on GPT-2 Small, Gemma-2B, and Llama-3-8B at practical sample sizes.
arXiv Detail & Related papers (2026-06-16T18:28:23Z) - From Attack Simulation to SIEM Rule: Deterministic Detection-as-Code Synthesis with Probe-Level Traceability [51.56484100374058]
Security teams simulate attacks against their own systems to check whether their monitoring would catch a real intruder.<n>A human bridges that gap by hand, reading each finding and writing the corresponding Sigma rule.<n>We show this translation can be partially automated when probes are drawn from a locked corpus.
arXiv Detail & Related papers (2026-06-03T14:26:25Z) - A Protocol-Language Model for Network Intrusion (Without Deep Packet Inspection) [0.0]
We present PLM-NIDS, which proves three claims in sequence.<n>Because it reads only IP/ TCP/UDP headers, it is inherently encryption-agnostic.
arXiv Detail & Related papers (2026-05-29T07:03:11Z) - TwinGate: Stateful Defense against Decompositional Jailbreaks in Untraceable Traffic via Asymmetric Contrastive Learning [60.68349524623048]
Decompositional jailbreaks pose a critical threat to large language models.<n>We present TwinGate, a stateful dual-encoder defense framework.<n>We construct a comprehensive dataset of over 3.62 million instructions spanning 8,600 distinct malicious intents.
arXiv Detail & Related papers (2026-04-30T13:44:01Z) - Beyond Input Guardrails: Reconstructing Cross-Agent Semantic Flows for Execution-Aware Attack Detection [32.301679396929536]
We propose SysName, a framework that shifts the defensive paradigm from static input filtering to execution-aware analysis.<n>SysName synthesizes fragmented operational primitives into contiguous behavioral trajectories, enabling a holistic view of system activity.<n> Empirical evaluations demonstrate that SysName effectively detects over ten distinct compound attack vectors, achieving F1-scores of 85.3% and 66.7% for node-level and path-level end-to-end attack detection, respectively.
arXiv Detail & Related papers (2026-03-04T01:59:16Z) - TraceGuard: Process-Guided Firewall against Reasoning Backdoors in Large Language Models [19.148124494194317]
We propose TraceGuard, a process-guided security framework that transforms small-scale models into robust reasoning firewalls.<n>Our approach treats the reasoning trace as an untrusted payload and establishes a defense-in-depth strategy.<n>We demonstrate robustness against adaptive adversaries in a grey-box setting, establishing TraceGuard as a viable, low-latency security primitive.
arXiv Detail & Related papers (2026-03-02T22:19:13Z) - When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift [0.0]
We present a comprehensive analysis using a benchmark of 18 datasets spanning harmful requests, jailbreaks, indirect prompt injections, and extraction attacks.<n>We propose Leave-One-Dataset-Out (LODO) evaluation to measure true out-of-distribution generalization.
arXiv Detail & Related papers (2026-02-15T14:21:43Z) - PADBen: A Comprehensive Benchmark for Evaluating AI Text Detectors Against Paraphrase Attacks [2.540711742769252]
We investigate why iteratively-paraphrased text evades detection systems designed for AIGT identification.<n>We introduce PADBen, the first benchmark systematically evaluating detector robustness against paraphrase attack scenarios.
arXiv Detail & Related papers (2025-11-01T05:59:46Z) - RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns [50.401907401444404]
Large language models (LLMs) are crucial for preventing misuse and building trustworthy AI systems.<n>We propose RepreGuard, an efficient statistics-based detection method.<n> Experimental results show that RepreGuard outperforms all baselines with average 94.92% AUROC on both in-distribution (ID) and OOD scenarios.
arXiv Detail & Related papers (2025-08-18T17:59:15Z) - Robust Anti-Backdoor Instruction Tuning in LVLMs [53.766434746801366]
We introduce a lightweight, certified-agnostic defense framework for large visual language models (LVLMs)<n>Our framework finetunes only adapter modules and text embedding layers under instruction tuning.<n>Experiments against seven attacks on Flickr30k and MSCOCO demonstrate that ours reduces their attack success rate to nearly zero.
arXiv Detail & Related papers (2025-06-04T01:23:35Z) - 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.