CyberSec Research

Code large language models increasingly retrieve external code context from repositories, documentation, issue threads, and coding-agent environments, creating an indirect prompt-injection surface where attackers hide instructions in comments, strings, identifiers, or decoy code. We propose CodeSentinel, a three-layer inference-time sanitizer. It uses Tree-sitter to extract high-risk model-facing CST nodes, then combines syntax-guided pre-filtering, CST-guided Dynamic Min-K\% scoring, and node perturbation analysis to detect adversarial and natural-looking semantic triggers. Detected nodes are removed or neutralized before reaching the downstream Code LLM. Across six recent attack families, \CodeSentinel achieves 0.80 average node-level F1, outperforming CodeGarrison, DePA, and KillBadCode.

Agent skills allow LLM-based coding agents to acquire domain-specific capabilities from third-party packages, but they also introduce a new supply-chain attack surface. We present PhantomSkill, an attack framework that hides malicious behavior in a skill's auxiliary resources rather than in its textual description. Its core technique, VulMask, rewrites overt malicious scripts into vulnerability-shaped implementations whose malicious behavior is activated only under attacker-controlled trigger conditions. This design shifts the visible signal from explicit malicious intent to ordinary-looking insecure code. Across representative host skills, attack goals, coding agents, generation models, and automated reviewers, VulMask preserves benign utility while reducing warning and malware-level detection compared with overt malicious scripts. Our results show that skill ecosystems require resource-level vetting, execution-time containment, and security policies that treat exploitable vulnerabilities in agent skills as potential malicious payloads.

Automated vulnerability discovery in large codebases remains challenging: traditional static analysis produces high false-positive rates, while dynamic approaches such as fuzzing require substantial infrastructure and often target narrow classes of bugs. Recent advances in large language models (LLMs) enable semantic reasoning about program behavior, but applying LLMs to repository-scale security analysis introduces challenges related to context management, cost, and verification. We present OpenAnt, an open-source vulnerability discovery system that integrates static program analysis with LLM-based reasoning in a multi-stage pipeline. OpenAnt introduces three key techniques. First, codebases are decomposed into self-contained analysis units filtered by reachability from external entry points, reducing the analysis surface by up to 97% while preserving attack-relevant code. Second, candidate vulnerabilities undergo adversarial verification through constrained attacker simulation, where the model evaluates exploitability under realistic attacker capabilities. Third, findings are validated through dynamic verification, in which exploit environments are generated automatically, executed in sandboxed containers, and discarded after use. Evaluation on widely used open-source projects including OpenSSL, WordPress, and Flowise shows that this architecture can identify previously unknown vulnerabilities while maintaining manageable analysis cost and substantially reducing false positives. Our results suggest that closed-loop vulnerability discovery pipelines, combining semantic reasoning with exploit validation, provide a practical path toward scalable automated security analysis. OpenAnt is released as open source under the Apache 2.0 license at https://github.com/knostic/OpenAnt.

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 for hiding the exchanged model parameters/gradients (e.g., by using cryptographic techniques), dealing with Byzantine contributions often requires inspecting the latter. Hence, most research works address these objectives separately. A recent line of work proposes to employ secure multi-party computation (MPC) to implement robust aggregators against model poisoning, thereby enforcing both confidentiality and Byzantine resilience. However, these solutions scale badly: they either require all-to-all communication between participants or delegate the entire computation to a small subset, whose computational and communication load grows proportionally with the size of the network. In this paper, we present Giskard, a protocol for confidential and Byzantine-robust decentralized aggregation. Giskard organizes $n$ parties into a tree of committees of size $O(\log n)$ and evaluates a coordinate-wise approximate median via a committee-adapted distributed binary search over the value domain, using BGW-style MPC within each committee. We assess Giskard both theoretically by proving its security and confidentiality properties and experimentally through extensive experiments involving up to one million participants. Compared to its closest competitors, Giskard reduces per-party communication complexity asymptotically while exhibiting comparable model utility under up to $n/4$ Byzantine parties.

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 prior work has treated structurally different designs as equivalent, though transmission-layer EA in carrier infrastructure (T-EA) and over-the-top EA at the platform layer (OTT-EA) differ in how cryptographic keys relate to ciphertext data. This paper builds a structured uncertainty framework for evaluating systemic compromise risk in EA architectures. It does not produce predictive forecasts, which the evidence cannot support; it separates findings robust to assumptions from those that depend on calibration. Four analytical layers are applied to T-EA and OTT-EA: three empirical pillars (historical analogues, a Monte Carlo scenario layer, a channel-independence decomposition) plus a Bayesian Structural Risk Model on a parallel-subgraph attack graph. The central findings are structural. First, EA-equipped architectures of either class carry strictly higher modelled risk than their no-EA counterfactual, an ordering independent of calibration. Second, the classes differ in distribution shape: T-EA risk is dominated by central tendency, OTT-EA by the tail under correlated campaigns. Third, calibration-conditional annual probability ranges span 1.4% to 12.9% for T-EA across the structured-judgement targeting-premium interval. Over multi-decade horizons, cumulative compromise is well above zero; key-material exfiltration is irreversible, weighing heavily on OTT-EA's larger user populations. The framework quantifies compromise probability, not expected harm; consequence modelling and benefit estimation are outside its scope.

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-community discourse are represented as a temporal evidence graph; a budgeted selector admits only a few evidence documents per CVE, and every score is paired with an auditable certificate listing the supporting signals, timestamps, source layers, and leakage flags. On 12012 prospective CVEs from public sources, budgeted evidence selection raises leakage-safe prospective recall@50 from 0.010 for a severity-only baseline to 0.026, while two evidence documents per CVE capture most of the value. A strong cross-encoder reranker lowers prospective recall to 0.016, showing that semantic relevance to a CVE is not the same as evidence of exploitation. Most importantly, a naive random split with unfiltered evidence inflates apparent prospective recall by 8.5x and EPSS-high recall by 5.0x. The main contribution is a leakage-safe evaluation protocol and reproducible evidence certificates for contestable vulnerability-prioritization claims.

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 method that combines a suspicious API knowledge base with an Agent workflow. PYPILINE first conducts static analysis on known malicious packages, extracting abstract syntax trees and generating API call graphs, from which it automatically extracts and constructs a structured suspicious API knowledge base. During the detection phase, this knowledge base is used to enhance reasoning capabilities. Through an Agent workflow, PYPILINE performs in depth semantic analysis of unknown packages and outputs a structured, interpretable maliciousness assessment report. The experimental results show that PYPILINE significantly outperforms existing state-of-the-art tools in precision of 96.7\%, recall of 99.6\%, and F1-score of 98.1\%, with its precision surpassing baseline tools by 5.7 to 24.2 percentage points. Additionally, we conducted an empirical study on malicious packages, systematically revealing prevalent attack strategies, as well as the most commonly abused APIs. Equipped with tool-calling AI agent workflows for automated vector database retrieval of suspicious API knowledge and mail server delivery of analysis reports, PYPILINE delivers a practical, efficient, and convenient malicious package detection solution to strengthen open-source ecosystem security.

We propose CHERI-D, an architectural extension to CHERI that supports efficient temporal memory safety. Efficient memory safety is an increasing priority for programming languages, operating systems, and hardware designs, and CHERI is a leading hardware/software system that provides native spatial safety and a foundation for temporal memory safety. Due to CHERI lacking intrinsic architectural support for temporal memory safety, the state-of-the-art CHERI temporal safety solution, Cornucopia Reloaded, is a software-based solution that provides use-after-reallocation (UAR) protections instead of the stronger use-after-free (UAF) mitigation, and suffers performance overhead due to delayed reallocation and revocation. CHERI-D associates object identification (ID) metadata with capability pointers to provide temporal integrity of allocations. CHERI spatial safety allows CHERI-D to store object IDs safely inline with allocation data, potentially within unused fragmentation. Evaluated in simulation and in hardware, CHERI-D significantly reduces the revocation overhead of Cornucopia Reloaded while allowing it to support strict use-after-free mitigation.

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 ability to generalize across frameworks and to detect novel exploitation paths. In contrast, we propose a solution that focuses on the effects an attack has on the host system executing the model and builds on foundational intuitions about ML model execution. In particular, we observe that ML models operate within well-defined lifecycle phases and that, within each phase, interactions with the host system are highly structured and predictable. We translate these intuitions into Moat, a dynamic lifecycle-aware approach for securing ML model execution, and instantiate this design in Re-Moat, our reference implementation. We evaluate Re-Moat across multiple ML frameworks using 77,974 real-world model artifacts from the Hugging Face Hub, 31 Proofs-of-Concept (PoCs) from CVEs, and 334 models from a state-of-the-art dataset, and compare it against state-of-the-art model-scanning solutions. Our results show that our approach detects all evaluated attack classes while maintaining a close-to-zero false-positive rate, validating our intuitions and motivating dynamic analysis for securing ML model execution.

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 who is actually at the keyboard. These two obligations are in fundamental tension. A model capable enough to use private information for task completion can, by the same capability, be induced to reveal it. To evaluate the trade-off of task accuracy and privacy leakage, we introduce Task-completion and Resistance to Active Privacy-extraction (TRAP). Each scenario includes a document containing private information, a task query that requires the agent to invoke the correct tool using private fields, and an attack query that attempts to elicit the same information in natural language. Evaluating 22 models spanning frontier proprietary and open-source models at multiple scales, we find that all model families exhibit non-trivial leakage, and that instruction-following ability correlates with leakage rate. Existing prompt-based defenses reduce leakage but at significant cost to task accuracy. Prompt optimization fails to escape this trade-off. We demonstrate that this failure is not incidental. For any softmax-based model, no soft-constraint defense, e.g., prompt-based defenses, can jointly achieve high task success with zero leakage probability. Motivated by this impossibility result, we propose structural private field isolation, which replaces private fields with hash keys before they reach the model. This approach largely prevents leakage while keeping task accuracy.

Conflict-free Replicated Data Types (CRDTs) ensure Strong Eventual Consistency without coordination, but typically assume benign participants and rely on validation or exclusion to handle Byzantine behavior. We address this problem through deterministic state reconstruction: rather than deciding which updates are admissible, all accepted updates are incorporated, while only a subset contributes to the reconstructed state. We instantiate this approach in Melda, a non-intrusive delta-state CRDT for JSON documents, and show that its reconstruction model guarantees convergence even under arbitrary update injection: adversarial updates are either structurally rejected or treated as inputs to the reconstruction process. We formalize this model and prove that replicas deriving state from the same set of updates cannot diverge despite equivocation, omission, or message reordering. We further show that authentication, authorization, and confidentiality can be layered without affecting convergence. Overall, this approach suggests that Byzantine tolerance can be achieved by decoupling update propagation from state derivation, allowing agreement on updates to be handled independently by external dissemination or consensus mechanisms.

Euclidean lattices are an interesting object of study in many regards and can have a rich structure arising from various constructions, e.g., from number field extensions. A particularly interesting class is the one of well-rounded lattices, as they relate to the well-known densest sphere packing problem in geometry, theta function minimization, and the famous Minkowski and Woods conjectures. In addition to being an important mathematical object in their own right, lattices also play a central role in many applications. This paper offers a survey of structured lattices and discusses their recent applications in lattice-based cryptography and secure wireless communications. Our goal is to spark the interest of mathematicians and adjacent communities in these fascinating topics in the intersection of lattices, number theory, cryptography, and wireless communications.

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 key components: (i) RTP-QoS, a Recurrent Trend Predictive Neural Network that learns and forecasts future Quality of Service (QoS) based on historical traffic patterns, and (ii) PDM, a Pairwise Decision Model that evaluates discrepancies between predicted and actual QoS to identify potential attacks. Evaluated on the public SDN-SlowRate-DDoS and CIC-IDS2017 datasets, IDQS respectively achieves over 79% and 91% detection accuracy across most attack scenarios with high recall and low false negatives, while maintaining an end-to-end inference time of just 0.28 seconds. The results demonstrate the effectiveness and efficiency of IDQS for real-time deployment in resource-constrained IoT environments.

Distributed large language model (LLM) inference frameworks connect isolated consumer-grade devices for large-scale model inference, substantially reducing hardware constraints. However, recent studies show that intermediate embeddings transmitted among participants can leak private prompts. As LLMs evolve into multimodal LLMs (MLLMs), this risk extends beyond text: image prompts contain rich visual and semantic information, making their intermediate embeddings highly privacy-sensitive. Yet, image-prompt leakage in distributed MLLM inference remains largely unexplored. In this paper, we investigate privacy risks to input images caused by intermediate embeddings in distributed MLLM frameworks. We first analyze the information flow from image pixels to intermediate representations. Since image and text embeddings are often intertwined across MLLM layers, we design an image embedding extraction algorithm as a prerequisite for reconstruction attacks, achieving 100% extraction accuracy across almost all MLLM layers in our experiments. Building on this, we develop two passive black-box image reconstruction attacks, MPAA and IEDA, reflecting realistic threats from normal participants with limited knowledge and capability. MPAA performs fine-grained pixel-level reconstruction via patch-wise information extraction and assembly, while IEDA performs coarse-grained semantic reconstruction through embedding-guided diffusion generation. We evaluate our attacks on four representative MLLM families: Gemma 3, Phi 4 Multimodal, Qwen 2.5 VL, and Llama 4 Scout. Results show consistently superior reconstruction performance in various settings. We further analyze the effects of MoE architecture, image preprocessing, model size, and text-image dependency on attack performance. To our knowledge, this is the first study of image reconstruction attacks on MLLMs.

Model-based learning agents use learned world models to predict future states, plan actions, and adapt to new environments. However, the process of updating world models from collected experience creates a training-time attack surface: adversarially poisoned fine-tuning trajectories can manipulate the learned dynamics and thereby corrupt downstream planning. In this paper, we propose SWAAP, the first two-stage data poisoning framework for learned world models. In the first stage, SWAAP identifies a harmful target world model that induces low-return behavior under planning while remaining close to clean dynamics, using first-order bilevel optimization enabled by a transition-gradient theorem. In the second stage, SWAAP realizes this target through stealth-constrained gradient matching, modifying only a limited fraction of fine-tuning transition targets so that the induced training gradients steer the victim model toward the adversarial target, while a prediction-error regularizer encourages the poisoned targets to remain close to the world model's natural approximation error. To assess attack stealthiness, we evaluate defenses and detectability across three stages of the poisoning pipeline: pre-training detection of poisoned transitions, robust training during fine-tuning, and test-time monitoring of the resulting world model. Across diverse continuous-control tasks, SWAAP causes substantial performance degradation while keeping poisoned transitions close to clean data and evading the evaluated non-adaptive residual/CUSUM/TRIM-style defenses. These results reveal a practical vulnerability in world-model adaptation pipelines and highlight the need for robustness methods that protect both world-model training data and learned dynamics.

Large language model (LLM)-based applications rely on system prompts to encode core logic and developer-defined constraints, making these prompts important intellectual property. However, system prompts are vulnerable to prompt leaking attacks. Although prior work has shown such attacks in controlled settings, their prevalence, causes, and defenses in real-world deployments remain unclear. This paper presents a systematic study of prompt leaking in real-world LLM-based applications. We measure 1,200 applications across six major commercial platforms and find that over 80% of deployments leak system prompts under realistic adversarial queries, sometimes exposing sensitive information such as third-party API keys. We also show that existing defenses often fail to prevent leakage without degrading usability. To explain these failures, we conduct an attention-level mechanistic analysis and identify attention drift, where query-key alignment bias and softmax amplification cause LLMs to progressively ignore defensive constraints. Guided by this insight, we propose AREA, a practical defense that re-anchors the model's attention using an optimizable soft prompt. Experiments and real-world case studies show that AREA matches the leakage resistance of state-of-the-art defenses while improving average usability by over 33% and reducing optimization overhead by nearly 3x. Our responsible disclosure led two affected vendors to classify these leaks as medium-severity vulnerabilities.

In enterprise environments, multiple Advanced Persistent Threat (APT) campaigns often unfold concurrently, producing audit logs in which attack techniques across actors (sources) are interleaved over time. This setting naturally gives rise to an Unknown-K Interleaved Sequence Demixing (UKISD) problem: recovering multiple latent campaigns from an interleaved technique sequence while jointly inferring their number and technique-level assignments. Existing approaches, ranging from statistical pattern mining to provenance-based analysis, typically assume single-campaign settings or rely on rigid heuristics, limiting their effectiveness under realistic conditions involving overlapping campaigns, shared techniques, and variable execution lengths. We present Topic-Guided Consistency Modeling (TGCM), a generative disentanglement framework to tackle the UKSID problem. TGCM leverages Consistency Models to learn a direct inverse mapping from interleaved multi-campaign observations to structured single-campaign sequences in a single inference step. To favor semantically coherent attack chains, TGCM incorporates a topic-guided prior derived from MITRE ATT\&CK narratives, providing high-level tactical constraints during decomposition. We evaluate TGCM on synthetic datasets, established mixed datasets, and incident traces from DARPA TC-E3 and TC-E5, comparing against 15 representative baselines spanning pattern mining, deep learning, and LLM-based methods. Results indicate improved separation robustness over baselines under heavy interleaving and technique sharing, and show that TGCM generalizes zero-shot to a naturally interleaved in-the-wild benchmark (DARPA TC-E5) without retraining.

The advent of agentic vulnerability detection is already becoming a watershed moment for software security. Audits conducted entirely by autonomous LLM agents are uncovering critical vulnerabilities in fundamental software underpinning digital society. Many of these vulnerabilities remained masked for years, surfacing only now with AI agents. Yet the reasoning behind these discoveries remains alarmingly opaque and unvalidated. What assumptions did the agent make about a function's inputs when it deemed that function to be secure? Failures in reasoning and incorrect assumptions can lead to missed vulnerabilities and reduce trust in agentic analysis. We propose a security-specification-first paradigm that (1) exposes the agent's tacit assumptions explicitly as security specifications and (2) continuously refines those specifications via runtime falsification. We realize our approach in Code-Augur, a novel harness for agentic vulnerability detection. Given a codebase, Code-Augur analyzes each component of the system for vulnerable code. When it deems a component to be secure, it commits the local invariants behind that judgment as in-source assertions. In parallel, Code-Augur leverages a guided fuzzer to attempt to falsify those assumptions. When the fuzzer triggers an assertion, this either reveals a genuine vulnerability or a flawed specification to refine. In both cases, this process grounds the agent's understanding, aligning its view of code intent with how the code actually behaves. On real-world subjects, Code-Augur effectively leverages security specifications to detect more vulnerabilities than other state-of-the-art agents. Additionally, Code-Augur found 22 new vulnerabilities in key open-source projects. Compared to curated specialized models like Claude Mythos, Code-Augur offers effective agentic vulnerability detection built on widely available LLMs like Sonnet and DeepSeek.