Where Did the Repair First Go Wrong? Localizing the Origins of Silent Failures in Agentic Vulnerability Repair

Localizing where an LLM-based agent first fails to uphold security during a repair can show which stage of its workflow needs an additional safeguard. This is difficult for silent failures, which are patches that pass syntactic and functional checks but still contain a security vulnerability...

Polar: LLM-Powered Synthesis of Real-World Cyber Evidence for Prioritization and Mitigation

Cyber threat analysis increasingly depends on evidence distributed across vendor advisories, vulnerability databases, and threat intelligence sources. Turning these fragmented observations into timely decisions requires models to connect technical severity with evolving exploitation evidence and...

Correct Verdicts, Flawed Reasoning: Structured Auditing of LLM-Based Vulnerability Reasoning

Large Language Models LLMs are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient; practitioners need explanations to triage bugs and engineer patches. Standard practice relies on Chain-of-Thought CoT prompting, but free-form reasoning...

Efficient Auditing of Adversarial AI Agent Behavior from Agent Traces

AI agents powered by large language models LLMs can perform complex tasks but may harm the systems they operate in, either intentionally or unintentionally. Existing agent monitoring approaches rely on rule-based guardrails or LLM-based trace auditing. However, rule-based guardrails can be bypass...

Understanding and Enhancing Backdoor Persistency in LLM Agent Post-Training

Developers can build LLM agents by adapting third-party models through benign post-training. We study a supply-chain threat in which an attacker supplies a model with a backdoor: hidden behavior that produces malicious outputs when a particular input pattern appears. Focusing on...

ai_bobao

aibobao AI-Powered Vulnerability Intelligence Feed Simp...

AgentDoxx: Agentic Re-Identification of Anonymized Text with Web Search

As Large Language Models LLMs gain tool use capabilities such as web search, they can retrieve and cross-reference public information, creating privacy risks beyond memorization. One manifestation is re-identification: linking an anonymized interview transcript to a named individual. Yet without...

ContractLens: Latent Security Knowledge for Malicious Smart Contract Detection

Detecting malicious smart contracts is essential to safeguarding the Web3.0 ecosystem. However, existing auditing methods based on large language models LLMs largely rely on prompt engineering or attack-specific fine-tuning, while the internal representations that support malicious logic detectio...

AutoDP-LLM: Automating Data Pre-Processing for Intrusion Detection Systems Using Large Language Models

The increasing complexity and scale of modern cyber-attacks demand intelligent and computationally efficient Intrusion Detection Systems IDS. However, designing effective data pre-processing pipelines traditionally involves substantial trial-and-error effort and repeated evaluation of alternative...

VulValidate: Auditing Function-Level Vulnerability Labels with Executable Evidence

Reliable learning-based vulnerability detection requires high-quality labels, yet datasets built from vulnerability-fixing commits may label functions as vulnerable simply because they were changed by a security patch. We present VulValidate, a framework that uses LLM agents to coordinate dynamic...

COPEX: Benchmarking LLM Robustness to Adversarial Context across Model Context Protocol Layers

Large language models increasingly mediate tool use in Model Context Protocol MCP systems, where adversarial influence may enter through user instructions, tool schemas, tool outputs, or protocol messages. Existing benchmarks often evaluate deployed agents, conflating model susceptibility with...

The Same Zero: Why Identical ASR Can Imply Different Guarantees in LLM-Agent Security

LLM-agent security has produced a dense landscape of defenses - prompt hardening, content filters, permission gates, sandboxes - yet no framework tells a deployer what a defense actually guarantees, or where that guarantee comes from. We apply Verification Autonomy Levels VAL - L0: LLM...

Xalgorix Autonomous AI Pentesting Agent 4.6.139

Most scanners detect. Xalgorix proves. An autonomous LLM agent works a full pentest methodology, then an independent verifier re-exploits every finding before it's reported - so you get proof, not a pile of maybes to triage. Self-hosted, private, and bring-your-own-LLM. Built in Go + TypeScript...

APEX: Active Protection at Execution Boundaries for LLM Agents

Indirect prompt injection IPI hides adversarial instructions in content that large language model LLM agents read at runtime. As agents compose heterogeneous capability units, including Tools, MCP servers, and Skills, the carriers of injection multiply, and defenses built to recognize attack...

skyvern v1.0.55

🐉 Automate Browser-based workflows using LLMs and Computer Vision 🐉 Skyvern automates browser-based workflows using LLMs and computer vision. It provides a Playwright-compatible SDK that adds AI functionality on top of playwright, as well as a no-code workflow builder to help both technical and...

Defense-In-Depth at the Perception-Reasoning Interface of LLM-Centric Agentic UAV Swarms

Large Language Models LLMs increasingly support Uncrewed Aerial Vehicle UAV swarm operations such as data collection scheduling, where the model reads structured sensor reports and decides which sensors to visit. An adversary who quietly manipulates those reports can redirect the swarm without...

PrivDev: Mapping Static-Analysis Data Types to DPV

Static-analysis scanners can identify personal-data types in source code, but they lack mechanisms to connect these findings to standardized privacy vocabularies. PrivDev maps 122 Bearer CLI data types to Data Privacy Vocabulary Personal Data DPV-PD categories and links them to potentially releva...

Xalgorix Autonomous AI Pentesting Agent 4.6.136

Most scanners detect. Xalgorix proves. An autonomous LLM agent works a full pentest methodology, then an independent verifier re-exploits every finding before it's reported - so you get proof, not a pile of maybes to triage. Self-hosted, private, and bring-your-own-LLM. Built in Go + TypeScript...

Beyond Predefined Sinks: Security-Aware Dependency Analysis for LLM Agents

Large language model LLM-based agents increasingly connect model-generated decisions to security-sensitive software capabilities such as command execution, filesystem access, network communication, browser control, and external tools. Existing analyses often use predefined sensitive operations as...

PT-2026-104401

crmne/ruby llm at commit fa6f279847d6d7027814539d9c0dfc3bbdfd2a83 contains a polynomial-time regular expression denial-of-service condition in Mistral model capability matching on Ruby 3.1.x...