REPORTS
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850 reports · page 12 of 22
arxiv_cs_cr · tlp:amber · 6/18/2026, 4:00:00 AM
A Predictive Neural Network Architecture for Early Detection of Low-Rate Cyberattacks arXiv:2606.18771v1 Announce Type: new Abstract: Low-Rate Denial of Service (LDoS) attacks pose a significant challenge to IoT networks due to their subtle and prolonged nature, often evading traditional intrusion detection systems. This paper presents IDQS (Intrusion Detection via QoS Prediction), a lightweight and proactive framework for early LDoS attack detection. IDQS integrates two new…
Read original ↗https://arxiv.org/abs/2606.18771arxiv_cs_cr · tlp:amber · 6/18/2026, 4:00:00 AM
TRAP: Benchmark for Task-completion and Resistance to Active Privacy-extraction arXiv:2606.18996v1 Announce Type: new Abstract: Agents are increasingly deployed in document-intensive workflows where sensitive private information is not an edge case but a routine input, e.g., an agent booking a flight needs passport numbers. In such settings, the agent must use private information to complete tasks accurately while never exposing it in its responses, because it cannot verify …
arxiv_cs_cr · tlp:amber · 6/18/2026, 4:00:00 AM
Lifecycle-Aware Dynamic Analysis for Secure ML Model Execution arXiv:2606.19023v1 Announce Type: new Abstract: The growing reliance on pre-trained Machine Learning (ML) models has introduced new attack surfaces. Recent vulnerabilities demonstrate that malicious behavior can be embedded within model artifacts, often bypassing existing defenses. Current model-scanning solutions primarily rely on static, format-specific rules or known attack signatures, which limit their abilit…
Read original ↗https://arxiv.org/abs/2606.19023arxiv_cs_cr · tlp:amber · 6/18/2026, 4:00:00 AM
PYPILINE: Malicious PyPI Package Detection via Suspicious API Knowledge and Agent Workflow arXiv:2606.19063v1 Announce Type: new Abstract: The detection of malicious PyPI packages is crucial for maintaining the security of the open source software supply chain. Existing methods, which primarily rely on rules or traditional machine learning, suffer from poor interpretability and difficulty in adapting to novel attacks. To address this, we propose PYPILINE, a novel detection m…
Read original ↗https://arxiv.org/abs/2606.19063arxiv_cs_cr · tlp:amber · 6/18/2026, 4:00:00 AM
Compute-Budgeted Exploitability Evidence Graphs for Prospective Vulnerability Triage arXiv:2606.19076v1 Announce Type: new Abstract: Defenders cannot patch every newly disclosed vulnerability at once, so exploitability prediction must be evaluated prospectively rather than retrospectively. We study compute-budgeted vulnerability triage in which each CVE is scored only from public evidence visible by a fixed decision time. Advisories, exploit archives, fix commits, and hacker…
Read original ↗https://arxiv.org/abs/2606.19076arxiv_cs_cr · tlp:amber · 6/18/2026, 4:00:00 AM
Quantifying Compromise Risk in Exceptional Access Architectures Under Sparse and Indirect Evidence arXiv:2606.19106v1 Announce Type: new Abstract: Lawful exceptional access (EA) systems hold the cryptographic keys that decrypt protected communications for authorised parties. The debate over their risks has been long and qualitative, complicated by two problems: no public dataset of EA-specific compromise events exists, so assessment must use sparse, indirect evidence; and pr…
Read original ↗https://arxiv.org/abs/2606.19106arxiv_cs_cr · tlp:amber · 6/18/2026, 4:00:00 AM
Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning arXiv:2606.19129v1 Announce Type: new Abstract: Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learning, clients train a machine learning model while keeping their data locally and share their model parameters or gradients with a set of neighbors. While enforcing confidentiality calls …
Read original ↗https://arxiv.org/abs/2606.19129arxiv_cs_cr · tlp:amber · 6/18/2026, 4:00:00 AM
Agentra: A Supervisable Multi-Agent Framework for Enterprise Intrusion Response arXiv:2606.18325v1 Announce Type: new Abstract: Enterprise intrusion response still depends on static playbooks and analyst-driven triage, creating delay between alert generation and containment. We present Agentra, a supervisable multi-agent Intrusion Response System (IRS) framework that converts alerts from IDS, EDR, and XDR platforms into structured incident response plans grounded in MITRE AT…
Read original ↗https://arxiv.org/abs/2606.18325arxiv_cs_cr · tlp:amber · 6/18/2026, 4:00:00 AM
Conflict-Aware Retriever Editing for Knowledge Injection Attacks on LLM-Based RAG Systems arXiv:2606.18310v1 Announce Type: new Abstract: Injecting malicious knowledge into retrieval-augmented generation (RAG) systems can manipulate retrieved evidence and mislead downstream generation, posing a serious security threat for AI applications. Existing RAG injection attacks mainly rely on manipulating external knowledge bases, such as crafting malicious corpus. However, the synth…
Read original ↗https://arxiv.org/abs/2606.18310arxiv_cs_cr · tlp:amber · 6/18/2026, 4:00:00 AM
TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization arXiv:2606.18312v1 Announce Type: new Abstract: Federated learning allows multiple clients to jointly train a shared model by sending gradient updates to a central server while keeping raw inputs local. However, prior gradient inversion attacks show that these updates can reveal enough information to reconstruct client inputs. Existing attacks on transformers either optimize dummy inputs to m…
Read original ↗https://arxiv.org/abs/2606.18312arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
OTRO: Oblivious Tokenization Path with Square-Root ORAM arXiv:2606.17358v1 Announce Type: new Abstract: The CPU-side large language model (LLM) tokenizer is a critical security gap in LLM serving through a confidential computing stack with CPU and GPU trusted execution environments (TEEs). Tokenizers converts the prompts through table-driven lookups, and the resulting memory access patterns are a powerful source of side-channel leakage. Recent work demonstrates end-to-end re…
Read original ↗https://arxiv.org/abs/2606.17358arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Structural Role Injection in Handlebars-Templated LLM Prompts: Triple-Brace Interpolation, Delimiter Family, and the Limits of HTML Auto-Escaping arXiv:2606.18120v1 Announce Type: new Abstract: Large language model applications build prompts from templates, and Handlebars is a widely used templating engine and the default prompt-template format in Microsoft Semantic Kernel. Its double-brace {{x}} expression HTML-escapes the interpolated value and is documented as the safe de…
Read original ↗https://arxiv.org/abs/2606.18120arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Securing Multi-Agent GIS Systems: Risk Evaluation and Prompt Hardening Optimization arXiv:2606.17092v1 Announce Type: new Abstract: Agentic systems are increasingly integrated with geographic information systems (GIS), where multi-agent coordination enables complex conversational and spatial analysis but introduces security risks. This work presents a security-oriented framework for risk identification, evaluation, and mitigation in a multi-agent GIS system while maintaining…
Read original ↗https://arxiv.org/abs/2606.17092arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Security and Human-Centered Assessment of BACnet-Controlled DALI Infrastructure in an Educational Building Automation Testbed arXiv:2606.17089v1 Announce Type: new Abstract: Building automation and control systems integrate heating, ventilation, air conditioning, lighting, sensing, and management functions through specialized communication protocols. While this integration enables flexible building operation, it also creates complex cyber-physical environments that are diffi…
Read original ↗https://arxiv.org/abs/2606.17089arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Bifrost: Hybrid TEE-FHE Inference for Privacy-Preserving Transformer and LLM Serving arXiv:2606.17421v1 Announce Type: new Abstract: Cloud-hosted transformer and large language model (LLM) inference creates a direct confidentiality problem: user prompts may contain sensitive code, business data, personal information, or regulated documents, yet remote serving exposes intermediate state to the cloud software stack and accelerator runtime. Fully homomorphic encryption (FHE) ke…
Read original ↗https://arxiv.org/abs/2606.17421arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
ARVO: Atlas of Reproducible Vulnerabilities for Open-Source Software arXiv:2606.17283v1 Announce Type: new Abstract: Achieving reproducibility, quantity, and diversity in vulnerability datasets has long been viewed as an inherent three-way trade-off, where improving one dimension often comes at the cost of the others. In practice, reproducibility has been the dimension most often neglected. This has limited what can be automatically extracted from historical bug datasets, an…
Read original ↗https://arxiv.org/abs/2606.17283arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Cache to the Future: A Distributed Webpage Archive for Internet Blackouts arXiv:2606.17245v1 Announce Type: new Abstract: Internet blackouts, occurring due to technological mishaps or intentional governmental action, prevent citizens from accessing the internet. Citizens in regions where internet blackouts are common have utilized blackout-resistant technologies to maintain communication. Such technologies often rely on mobile mesh networks to provide limited messaging servi…
Read original ↗https://arxiv.org/abs/2606.17245arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Safety, Security, and Cognitive Risks in Neuro-Symbolic AI arXiv:2606.17223v1 Announce Type: new Abstract: Neuro-symbolic AI (NeSy) pairs neural perception with symbolic reasoning, making it attractive for high-stakes domains where explainability and structured inference are required. However, this hybrid architecture introduces an enlarged attack surface spanning five layers: neural perception, symbolic knowledge bases, reasoning engines, agentic orchestration, and data sto…
Read original ↗https://arxiv.org/abs/2606.17223arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Beyond Native Success: Auditing Deployment-Interface Exposure of CLIP Backdoors arXiv:2606.17815v1 Announce Type: new Abstract: Contrastive Language-Image Pre-training models are widely reused across downstream interfaces, including feature extraction, retrieval, reranking, and selection. Existing CLIP backdoor, however, usually validate attacks on a small attack-native task, leaving unclear whether the same poisoned checkpoint remains exposed, weakens, or becomes not applic…
Read original ↗https://arxiv.org/abs/2606.17815arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
LineageMark: Multi-user White-box Watermarking for Contribution Tracing in Model Derivation Chains arXiv:2606.17123v1 Announce Type: new Abstract: In open large language model (LLM) ecosystems, models are frequently adapted across multiple domains and applications, forming multi-stage derivation chains. Consequently, tracking and verifying historical contributions is essential for model provenance and intellectual property protection. However, existing watermarking methods a…
Read original ↗https://arxiv.org/abs/2606.17123arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
TrustErase: Auditable Instant Machine Unlearning with Passport-Embedded Representations arXiv:2606.17122v1 Announce Type: new Abstract: The demand for privacy-compliant AI has amplified the need for machine unlearning; yet, existing retraining or distillation-based methods remain unverifiable and computationally costly. We introduce TrustErase, a verifiable, data-free unlearning framework leveraging passport-embedded representations for instant, modular, and auditable forget…
Read original ↗https://arxiv.org/abs/2606.17122arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
An Empirical Analysis of AI Slop in Music Streaming arXiv:2606.18052v1 Announce Type: new Abstract: Generative AI models lower the bar for content creation, making it easy for any user to create professional-looking images, text and music with minimal effort. This has enabled a new cottage industry around creation of "AI slop" mass quantities of mediocre content produced to generate revenue, often through misrepresentation as human-authored content, or scams involving automa…
Read original ↗https://arxiv.org/abs/2606.18052arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Graph neural networks at war: integrating cybersecurity and drone intelligence in the Israeli-Iranian conflict arXiv:2606.17119v1 Announce Type: new Abstract: Physical cyber systems have brought about new threats and challenges in detection and immediate response. This study examines how Graph Neural Networks (GNNs) can be used to aid cybersecurity and drone management in a physical cyber system comprising of cyber intrusions and unmanned aerial vehicles (UAVs). By providing…
Read original ↗https://arxiv.org/abs/2606.17119arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
ShellGames: Speculative LLM-Driven SSH Deception arXiv:2606.17986v1 Announce Type: new Abstract: Cyber deception and Moving Target Defense are promising strategies that aim to disrupt adversaries by increasing uncertainty. However, sustaining long-lived, credible interactive sessions with adversaries remains an open challenge. Large Language Models (LLMs) offer a promising path toward more dynamic deception systems, but suffer from key limitations that fundamentally limit th…
Read original ↗https://arxiv.org/abs/2606.17986arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Quantifying quantum risk: a measure of crypto agility arXiv:2606.17116v1 Announce Type: new Abstract: Because of their ability to enable new forms of cryptanalysis, quantum computers pose a threat to the cryptographic algorithms that are widely used to secure contemporary computer systems. A practical quantum computer may emerge within the next ten years or so, but due to theorised "harvest now, decrypt later" style attacker behaviour, mitigations are necessary today. Recent…
Read original ↗https://arxiv.org/abs/2606.17116arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Anywhere, Any-Stymie: Remote Activation of Trojan Malware on LiDAR with Modulated Signals arXiv:2606.17562v1 Announce Type: new Abstract: LiDAR sensors are widely deployed in autonomous systems for 3D perception and safety-critical decision-making. We identify a previously unexplored attack surface in which dormant malware embedded in the LiDAR sensing pipeline remains inactive during normal operation and can be externally triggered after deployment, without requiring access…
Read original ↗https://arxiv.org/abs/2606.17562arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
An Evaluation of Data Leakage Risks in Tool-Using LLM Agents in Realistic Scenarios arXiv:2606.17114v1 Announce Type: new Abstract: AI agents are increasingly being adopted in enterprise and personal settings with access to emails, databases, documents, and other tools where they can read, update, and disseminate sensitive information. Much of prior research on data leakage risks in agents has focused on adversarial data exfiltration through prompt injections and jailbreaks.…
Read original ↗https://arxiv.org/abs/2606.17114arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
An AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts arXiv:2606.17555v1 Announce Type: new Abstract: Banks simultaneously face signature-based fraud (card-not-present attacks, account takeover, ATM cloning) and behavioural financial crime (structuring, layering, mule networks, business email compromise) -- two threat families with fundamentally different detection requirements. Static rule engines that reliably catch bru…
Read original ↗https://arxiv.org/abs/2606.17555arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Fractional Verkle Trees: A Hypertree Decomposition and Verified Proof Serialization Architecture for High-Performance Blockchain State Accumulators arXiv:2606.17111v1 Announce Type: new Abstract: Modern blockchain state management faces a critical scalability bottleneck: maintaining cryptographic commitments over hundreds of millions of entries becomes computationally prohibitive. Ethereum's transition to Verkle Trees: polynomial commitment accumulators reducing proof sizes …
Read original ↗https://arxiv.org/abs/2606.17111arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs arXiv:2606.17110v1 Announce Type: new Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user conversations containing secrets. Ensuring the privacy of such data against extraction attacks has become a central concern. In this paper, we ask whether an attacker who can poison a portion of the training data can…
Read original ↗https://arxiv.org/abs/2606.17110arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
SNAS: A Multi-Layer Defense-in-Depth Architecture for Secure Egress in Sandboxed Workloads arXiv:2606.17533v1 Announce Type: new Abstract: Snowpark enables data engineering and AI/ML workloads in Snowflake by executing user-defined functions in secure sandboxes. Many of these workloads require external connectivity to access cloud APIs, external databases, or feature stores, creating a dependability challenge: how to provide transparent network access while preserving strict…
Read original ↗https://arxiv.org/abs/2606.17533arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
SoK: AI-Augmented Binary Reversing arXiv:2606.17398v1 Announce Type: new Abstract: Binary reversing is fundamental to software understanding, vulnerability discovery, malware investigation, and firmware auditing. However, it remains inherently challenging due to the irreversible loss of semantic information during compilation. Recent advances in machine learning, large language models (LLMs), and agentic AI systems have accelerated the adoption of AI-augmented binary reversi…
Read original ↗https://arxiv.org/abs/2606.17398arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detection arXiv:2606.17109v1 Announce Type: new Abstract: Given their effectiveness in modeling the relational structure among network traffic flows, graph neural networks (GNNs) have been widely adopted in network intrusion detection systems (NIDSs). However, most existing GNN-based NIDS approaches focus on the relational structure of traffic flows, and treat them as temporally independent, whi…
Read original ↗https://arxiv.org/abs/2606.17109arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
Children Are Not the Enemy: Child-Fit Security as an Alternative to Bans and Surveillance arXiv:2606.17957v1 Announce Type: new Abstract: Digital technologies are now central to children's learning, play, communication, identity formation, and social participation. Yet dominant approaches to children's online safety often rely on containment mechanisms, including bans, age gates, parental controls, monitoring, and screen-time restrictions. These approaches can be useful in s…
Read original ↗https://arxiv.org/abs/2606.17957arxiv_cs_cr · tlp:amber · 6/17/2026, 4:00:00 AM
PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents arXiv:2606.17467v1 Announce Type: new Abstract: Prompt injection defenses evaluated on synthetic benchmarks do not generalize to real enterprise documents, which are longer, denser, and interleave legitimate authority language with factual content. We demonstrate this gap with a real-document benchmark of 122 tasks across five professional domains (financial, legal, medical, scientific, DevOps)…
Read original ↗https://arxiv.org/abs/2606.17467arxiv_cs_cr · tlp:amber · 6/16/2026, 4:00:00 AM
A Security Analysis of Long-Horizon Agentic AI Systems: Threats, Evaluation, and Framework Development arXiv:2606.14816v1 Announce Type: new Abstract: This paper presents a structured analysis of security challenges in long-horizon agentic AI systems. The study reviews existing threats, evaluation approaches, attack propagation mechanisms, and security frameworks. A taxonomy of security threats and a framework for analyzing attack propagation are proposed to support future r…
Read original ↗https://arxiv.org/abs/2606.14816arxiv_cs_cr · tlp:amber · 6/16/2026, 4:00:00 AM
VLALeaks: Membership Inference Attacks against Vision-Language-Action Models arXiv:2606.15165v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models enable end-to-end robot control and have garnered widespread attention. However, the memorization of training data inherent to VLA, coupled with the high cost of robotic data acquisition, raises serious concerns regarding data privacy leakage and intellectual property infringement. Membership inference attacks (MIAs)…
Read original ↗https://arxiv.org/abs/2606.15165arxiv_cs_cr · tlp:amber · 6/16/2026, 4:00:00 AM
Data-Centric Benchmarking of Exploit Generation in LLMs: Understanding the Impact of Fine-Tuning arXiv:2606.15123v1 Announce Type: new Abstract: We study the task of CVE-conditioned exploit generation, where a model drafts proof-of-concept (PoC) exploits given software vulnerability context. We adopt a data-centric approach, constructing a high-quality dataset via multi-stage preprocessing and introducing a scalable evaluation framework with LLM-as-judge and fine-grained rub…
Read original ↗https://arxiv.org/abs/2606.15123arxiv_cs_cr · tlp:amber · 6/16/2026, 4:00:00 AM
Is Your Agent Playing Dead? Deployed LLM Agents Exhibit Constraint-Evasive Fabrication and Thanatosis arXiv:2606.14831v1 Announce Type: new Abstract: This paper presents and characterizes a spectrum of previously unreported behaviours we term Constraint-Evasive Fabrication (CEF): when an LLM agent operates under irreconcilable constraints (where no response can simultaneously satisfy all active rules) it spontaneously fabricates plausible external obstacles and presents them…
Read original ↗https://arxiv.org/abs/2606.14831arxiv_cs_cr · tlp:amber · 6/16/2026, 4:00:00 AM
Fuzzy PSI from Symmetric Primitives with Exact Logarithmic Dependence on Distance Threshold arXiv:2606.15093v1 Announce Type: new Abstract: Previous FPSI works have demonstrated a linear scaling with the distance threshold $\delta$, while some recent works have achieved a poly-logarithmic dependence on $\delta$. However, these protocols either support only the $L_\infty$ distance, or they support general $L_{p\in[1,\infty]}$ distances but rely on expensive additive homomorph…
Read original ↗https://arxiv.org/abs/2606.15093