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 built for penetration testers and bug bounty hunters. It automates the entire workflow from information gathering to report generation without removing analyst control.
Most agentic AI security tools suffer from hallucinated findings, poor context retention, and weak tool integration. PentesterFlow tackles these problems with built-in pentesting skills, evidence-based vulnerability confirmation, and continuous local learning capabilities.
The tool connects to local or hosted large language models such as Ollama, LM Studio, Kimi, Groq, Gemini, DeepSeek, and OpenRouter. It plans actions against defined targets, runs real security testing tools, and always requests explicit analyst approval before executing sensitive commands.
Core Capabilities
PentesterFlow covers the full penetration testing lifecycle: scoping, reconnaissance, enumeration, vulnerability validation, coverage tracking, reporting, and ongoing learning.
- Model backends: Ollama, LM Studio, Kimi, Groq, Gemini, DeepSeek, OpenRouter and OpenAI-compatible APIs
- Built-in skills: Recon, Web vulnerabilities, SSRF, SSTI, JWT, GraphQL, race conditions, subdomain takeover, Supabase, deserialization
- Toolset: Shell/Bash, HTTP, Burp Suite bridge, browser capture, MCP support, file operations, grep/glob
- Report output: Confirmed findings with PoCs, impact analysis, remediation advice, and ready-to-use curl commands
In a live demonstration, the tool loaded a web vulnerability skill module, sent HTTP requests against an order API, and automatically confirmed a high-severity IDOR vulnerability, writing the evidence-backed finding directly to a Markdown file.
Security and Integration
PentesterFlow enforces permission-based tool calls, blocks dangerous shell command patterns, and redacts credentials during compression and snapshot operations. It also offers a YOLO mode that auto-approves all actions inside isolated lab environments.
The tool integrates directly with Burp Suite through a companion bridge, allowing testers to import captured traffic into the CLI and export confirmed findings back as Burp issues.
Installation and Usage
Installation uses a simple shell script on macOS/Linux or PowerShell on Windows to fetch the latest standalone binary and verify its SHA-256 checksum. Users can pin specific versions, select local models such as Ollama’s qwen2.5-coder, or connect to hosted providers, then set targets with the /target command and issue natural-language instructions.
Analysts working on sensitive targets are reminded that PentesterFlow is intended solely for authorized security testing, as approved commands can execute real shell operations and send live HTTP requests.
Industry Positioning
Alongside projects such as PentAGI and PentestGPT, PentesterFlow distinguishes itself through transparent, reproducible evidence chains and mandatory analyst approval, making it suitable for security teams cautious about fully autonomous penetration testing agents.
Related articles
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.
Optimizing Cybersecurity Content for LLMs: How Sites Can Enter Generative AI Answers
Search engines and AI services like ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews increasingly deliver synthesized answers instead of link lists. For cybersecurity publishers this changes competition because high traditional rankings no longer guarantee visibility or accurate citation. The article explains GEO, AEO and LLMO practices, shows how material moves through indexing, fragment selection and summarization stages, and stresses the need for self-contained facts that survive extraction and paraphrasing. It provides concrete writing frameworks for vulnerability reports, including required fields such as CVE identifiers, affected versions, attack conditions and real-world exploitation evidence. Technical requirements cover correct robots.txt handling for Googlebot, OAI-SearchBot, GPTBot and Bingbot plus the use of IndexNow for rapid updates. The piece also warns about poisoning risks, prompt injection and slopsquatting attacks that can feed false data into generative systems.
LangGraph Architecture Combines Hybrid RAG with YARA and Sigma Engines for Streaming Log Analysis
A new architectural pattern integrates LangGraph with a hybrid RAG system and deterministic signature engines to process large volumes of unstructured cybersecurity logs efficiently. The pipeline uses Vector for chunking logs into 250-line segments with 20-line overlap, Kafka for streaming, and an 8-node asynchronous DAG that runs AI and rule-based branches in parallel. Agent 1 groups events and generates hypotheses, while a hybrid RAG module performs query reformulation, vector plus BM25 search with 0.6/0.4 weighting, and LLM re-ranking against a translated MITRE ATT&CK knowledge base stored in ChromaDB. Parallel YARA and Sigma engines scan parsed logs using custom text-based rule implementations, with automatic YARA rule generation triggered when coverage gaps are detected. Final aggregation occurs in Agent 3, which validates findings, deduplicates confirmed incidents, and routes unconfirmed events for manual review while storing reports in PostgreSQL. Tested on a 43-minute synthetic dataset containing 38 MITRE techniques, the system achieved 85.7% precision and 78.9% recall at 3.5 lines per second using Gemini 2.5 Flash.
Local LLM Deployment for SOC: How Many Incidents Can One NVIDIA RTX PRO 6000 Handle?
In the second part of the experiment, R-Vision analysts tested the Qwen3.5-122B-A10B-GPTQ model running locally with vLLM on an NVIDIA RTX PRO 6000 Blackwell Max-Q GPU with 96 GB VRAM. The evaluation moved from synthetic stress tests to realistic SOC workloads using anonymized real incidents from their internal operations center. Tasks included incident ranking, summarization, similarity search, retrospective analysis, and preliminary verdict generation within the R-Vision SOAR orchestration pipeline. Two load scenarios were modeled: a calm shift with 10-15 incidents per hour handled by 3-5 L1/L2 analysts, and a peak scenario with 50-100 incidents arriving in a short period involving 5-7 analysts. Resources were split between a high-priority interactive chat pool and a lower-priority background SOAR pool using an AI Gateway layer to manage queues, context length, and KV-cache usage. Results showed that one GPU can process up to 5 incidents simultaneously in the background pipeline, delivering a theoretical maximum throughput of 300 incidents per hour under the tested conditions and prompt profiles.