Ollama

Local LLMs in AppSec: What Open Tools and Models Can Handle in Application Security

The article examines how local large language models can be integrated into application security workflows without relying on cloud subscriptions. It traces the evolution from llama.cpp in 2023 through agentic frameworks like PentAGI and Strix in 2025-2026. Teams can now run models on local hardware, connect them to code analyzers such as Semgrep, and delegate specific tasks like vulnerability path reconstruction or multi-step application testing. The piece highlights practical scenarios including static code review, dynamic testing with browser and terminal agents, and security evaluation of embedded AI assistants using tools like Garak and PyRIT. It stresses the importance of task decomposition, context management, and verification of model outputs to avoid misleading results. Hardware performance, model selection, and custom agent configurations are presented as key factors for successful adoption.

AI Agents Becoming Core InfoSec Skill Like Microsoft Office Proficiency

Working with AI in information security is shifting from a specialized skill to a baseline expectation similar to using Microsoft Office tools. The key difference lies in the cost of errors: poor Word usage wastes time, while mishandling AI agents burns subscription limits and corporate funds. The article details practical workshops from the Engineering October series covering AI agents from beginner tools to securing the agents themselves. Core concepts include distinguishing models from agents, managing context windows to prevent forgotten instructions, and using skills, MCP connectors, and sub-agents for efficiency. Real-world examples show agents handling CVE analysis, alert triage, and threat report reviews while highlighting risks like hallucinations and data exposure. Guidance covers safe deployment in isolated environments and the irreplaceable role of human expertise in strategy and verification.

How to Interact with AI Models Without Exposing Sensitive Data

The article provides practical guidance on minimizing data leakage risks when using popular AI chatbots such as ChatGPT, Gemini, Claude and GigaChat. It explains that conversations are routinely scanned by automated filters and may be reviewed by human moderators or shared with law enforcement upon request. Key recommendations include disabling model training on user data, replacing sensitive values with placeholders, regularly deleting chat histories and verifying downloaded models for malicious injections. The guide also demonstrates local deployment using Ollama and secure API integration through the ChatBox client with Cloud.ru’s Evolution Foundation Models service. Local execution in Docker containers is presented as the most private option, although it requires significant computational resources. The author stresses that even after disabling training, data may still reach moderators and that users remain responsible for their own information.

NVIDIA NemoClaw Flaw Lets Malicious Webpage Hijack Local Ollama Models via DNS Rebinding

Oasis Security disclosed a critical attack chain in NVIDIA NemoClaw that allows a malicious webpage to silently take over a local Ollama instance and poison AI model chat templates. The vulnerability stems from NemoClaw binding Ollama to 0.0.0.0:11434 on Windows without authentication, combined with skipped Host header checks and permissive CORS. Attackers use DNS rebinding to reach the local API from the browser and then inject persistent hidden instructions through the /api/create endpoint by modifying Go templates. These poisoned templates append attacker commands to every system message and survive across sessions and new prompts. No CVE has been assigned and no official patch exists, though version v0.0.106 added an incomplete bind check that can be disabled via environment variable. The issue revives a similar problem previously fixed in Ollama under CVE-2024-28224. Oasis Security notes this marks their third successful compromise of local AI agents using the same browser-to-local-API pattern.

Hermes Emerges as Modular Harness for Practical AI Security Testing

The article details the development of Hermes, a lightweight modular harness designed to turn raw language models into reliable AI security agents. Unlike heavy frameworks such as OpenClaw, Hermes uses dynamic skill loading, strict runbooks stored in Git, and a file-system source of truth to eliminate hallucinations and context bloat. It continuously scans dozens of sources including NVD, CISA KEV, arXiv and research blogs, then applies the custom TIPS scoring metric to rank threats before attempting reproduction. A dedicated Paper-to-PoC skill converts academic attack descriptions into executable code that is validated inside an isolated Ollama and Docker sandbox. Eight mandatory validation gates must pass before any finding is accepted, ensuring reproducibility and freshness of data. The system already supports tools such as Garak, PyRIT, promptfoo, fickling, modelscan and presidio, and evolves autonomously by committing successful attack scenarios back to its repository.

AI Coding Tools Under Fire: Grok Build Uploads Entire Git Histories, Claude Code Suspected of Silent Transfers

Security researcher cereblab uncovered that Grok Build 0.2.93 establishes separate HTTPS channels to exfiltrate full Git repositories, resulting in a 27800-fold traffic discrepancy between task context and storage uploads to Google Cloud Storage buckets. The tool ignores user instructions such as "do not read" and decouples the improve_model_enabled client switch from the server-controlled trace_upload_enabled flag, allowing continued uploads even when privacy settings are disabled. Similar concerns emerged around Claude Code, which maintains undisclosed WebSocket connections that transmit file paths, dependency trees, and code metadata without user awareness or audit logs. Comparative traffic audits showed that Codex and Gemini produced no anomalous outbound activity, while Grok Build and Claude Code were the only tools confirmed to perform data transfers beyond user authorization. The incidents highlight systemic issues including server-side remote control of client behavior, lack of third-party audits for closed-source binaries, and the conflict between model training data needs and user data sovereignty. Experts recommend zero-trust measures such as network blocking, Docker sandboxing without mounting .git directories, git filter-repo sanitization, and preference for auditable open-source alternatives like Continue.dev or locally deployed Ollama models.

PentesterFlow Launches Open-Source AI CLI Tool for Penetration Testers and Bug Bounty Hunters

PentesterFlow is a new open-source, human-in-the-loop AI command-line tool designed specifically for penetration testers and bug bounty hunters. It automates the full workflow from reconnaissance to report generation while requiring explicit analyst approval before executing sensitive commands. The tool addresses common issues in agentic AI security tools such as hallucinations, weak context retention, and poor tool integration by incorporating built-in pentesting skills and evidence-based vulnerability confirmation. It supports connections to local or hosted LLMs including Ollama, Gemini, Groq, and others, and features continuous local learning that stores user preferences and lessons without retraining models. A key differentiator is its integration with Burp Suite and a permission-based execution model that includes a YOLO mode for isolated environments. The project positions itself as a transparent alternative to fully autonomous tools like PentAGI and PentestGPT.