Xalgorix Autonomous AI Pentesting Agent 4.6.113

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...

Similarity Is Not Validity: Defending LLM Semantic Caches against Poisoning

Semantic caches reduce LLM serving costs by reusing previously generated answers for semantically similar queries. However, retrieval is based solely on embedding similarity between the incoming query and cached queries. This design enables cache poisoning: an attacker can cache a malicious...

Self-Evolving Defense: Continual Security Policy Learning for LLM Agents

Large language models LLMs increasingly power agents that access sensitive information, use external tools, and modify software repositories. Although these capabilities offer substantial benefits, they also create security risks such as jailbreaks, prompt injection, and vulnerable code generatio...

When Consent Outlives Context: Residual Authority Replay in Long-Lived Agents

LLM agents increasingly rely on user approval to authorize security-sensitive actions at runtime. Such approvals are granted within a specific task and execution context. In long-lived agents, authorization decisions may need to persist across tasks or sessions. We find that this continuity can...

Can Prompt Anonymity Protect Your Identity from LLM Providers?

User conversations with large language models LLMs often contain highly sensitive personal information that can be exploited by LLM providers to create detailed user dossiers, enable targeted advertising, and train more powerful models. To protect user privacy, anonymizing LLM proxies have emerge...

API Secrets Should Never Become Tokens in the LLM's Vocabulary

Tool-using large language model LLM agents turn credential hygiene from a storage problem into an execution-security problem. A key pasted into a prompt, or embedded in a system prompt or tool configuration, crosses from an authentication boundary into a data pipeline, where it may persist in...

Climbing the Hill: Prompt Injection Red-Teaming against Frontier Models with Curriculum Reinforcement Learning

Prompt injection is a leading security risk for LLMs and LLM-based applications such as agents. State-of-the-art red-teaming methods for prompt injection leverage reinforcement learning RL to train an attacker LLM to generate effective injected prompts. However, when targeting frontier LLMs such ...

HESP: Separating What to Probe from When to Stop in Local LLM Alert-Triage Agents

Security operations centers receive far more alerts than analysts can investigate, and organizations that cannot send their telemetry to hosted models must automate triage with small open-weight LLMs on their own hardware. Current LLM agents leave the investigation procedure to the model, and sma...

Xalgorix Autonomous AI Pentesting Agent 4.6.107

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...

ORBIT: A Framework for Multi-Agent Safety and Security Evaluations

Multi-agent LLM systems are increasingly deployed for complex, long-horizon tasks or emerge as a natural consequence of agents interacting in the wild. Yet they give rise to significant safety and security risks: the flexible protocols that enable task generalization also expose novel threats, fr...

Xalgorix Autonomous AI Pentesting Agent 4.6.99

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...

ethibench

From Controlled to the Wild: Evaluation of Pentesting Agents in the Real-World AI pentesting agents are increasingly credible as offensive security systems, but current benchmarks still provide limited guidance on which systems will perform best on real-world targets. Most existing evaluations...

Resource-Optimized and Energy-Aware Agentic AI Framework Anchored on Blockchain for Secure Software Supply Chains

This paper proposes a blockchain-backed agentic security framework designed to safeguard the complete software development lifecycle SDLC while also securing the agentic AI components responsible for monitoring it. The framework coordinates a set of specialised security agents, covering source...

FragToken: Amplifying LLM Inference Costs through Noncanonical Token Generation

As large language model LLM inference becomes increasingly expensive, resource-consumption attacks pose a growing threat to model providers. Existing attacks typically amplify cost by inducing abnormally long or repetitive outputs on attacker-controlled or triggered requests, making them easier t...

AGATE: Provenance-Based Runtime Defense against Compositional Attacks on LLM Agents

LLM agents can produce harmful effects through sequences of ordinary operations. Judging such actions requires establishing both the authority that permits them and the origin of the data they carry. We present AGATE, an authorization and data-provenance gate at instrumented agent-harness...

AuthGuard-R: Safety-Compliant Mission Hijacking and Dual-Gate Defense for LLM-Controlled Robots

Large language models are increasingly used as high-level planners for mobile robots, robot manipulators, and autonomous vehicles. Recent studies show that these systems can be influenced through malicious text, speech, visual instructions, retrieved documents, and poisoned sensory context. Most...

Xalgorix Autonomous AI Pentesting Agent 4.6.95

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...

Configuration, Not Conscience: A Large-Scale Empirical Study of LLM System Prompts

Leaked system prompts are often treated as windows into the hidden values of commercial language models, yet their composition is rarely studied at scale. We analyze a merged corpus of 407 leaked, reconstructed, or officially published system prompts from 62 vendors across four community...

MetaPermit: Scalable and Auditable Access Control for AI Agents Via LLM-Inferred Meta-Attributes

The rise of autonomous AI agents equipped with tools has introduced significant security risks, ranging from unintended tool misuse to adversarial manipulation through Indirect Prompt Injection IPI attacks. In practice, deployed agent systems such as OpenAI Codex and Claude Code protect tool...

AI-powered fuzzing with the GitHub Security Lab Taskflow Agent

If you 're new to fuzzing and want to learn the fundamentals first, check out our Fuzzing 101 course at gh.io/fuzzing101. Continuous fuzzing is not a magic solution that solves all your problems . Even projects that have been enrolled in OSS-Fuzz for years can still hide critical bugs, and the...