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BugBase Pentest Copilot Enterprise automates black-box pentesting

BugBase launched Pentest Copilot Enterprise, an autonomous AI black-box pentesting platform using parallel agents to attack 100 vulnerability classes with validated PoCs.

BugBase announced Pentest Copilot Enterprise, which performs black-box red teaming without source-code access while maintaining authenticated context. Parallel specialized agents map pages, APIs, accounts and business functions, then execute iterative attacks across 100 vulnerability types including authentication, injection and business-logic flaws. The tool uses real Chromium browsers to preserve cookies, tokens, CSRF state and multiple identities, and navigates WAFs, bot detection, CAPTCHA and T-OTP. BugBase claims full scope coverage on OWASP Juice Shop, Broken Crystals and GOAD, NHA, and DRACARYS Active Directory labs.

Help Net Security · 11d agoTools

Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection

Researchers unveil Repeat-After-Me, a black-box visual prompt injection achieving over 80% success on Qwen3.6-27B and 47% on GPT-5.5.

Researchers present Repeat-After-Me, a black-box adaptive visual prompt injection that induces frontier VLMs to reveal PII or make malicious tool calls via injected images. It exceeds 80% attack success rate on Qwen3.6-27B and 47% on GPT-5.5 even when the benign user prompt is unrelated and does not authorize the injected task. In a real-world OpenClaw Discord deployment, a minimally injected image can overwrite TOOLS.md, enabling later remote code execution and secret exfiltration.

arXiv cs.CR · 12d agoAI safety & security

Forging Tree-Ring: Reproducing and Instrumenting Black-Box Semantic Watermark Forgery

Reprompt watermark forgery reproduces on Stable Diffusion XL using free-tier T4 GPUs, with forged images accepted by the genuine detector 5 of 6 times.

The authors reproduce the Reprompt forgery attack of Müller et al. against Tree-Ring watermarking on Stable Diffusion XL using the released code on free-tier dual T4 GPUs with 14.6 GB usable memory, versus the A40 hardware of the original study. Over six trials, the genuine detector flagged genuine images 6/6, clean images 0/6, and forged images 5/6, at 325-332 seconds per attack. They also recovered the detector's discarded non-central chi-square statistic and built two natural scores separating forged images from the clean null at AUC 0.861 and 0.972. The notebook, pinned fork, and all measurement artifacts are released with the paper.

arXiv cs.CR · 4d agoResearch

RAPID: A Real-Time Defense Against Unauthorized Model Distillation for Text-to-Image Services

RAPID embeds defensive perturbations in a T2I model's shared VAE decoder to block unauthorized black-box distillation in real time.

The paper defends text-to-image services against model theft via black-box output-based distillation, where adversaries collect prompt-image pairs to train substitute models. RAPID integrates defensive perturbations into the shared VAE decoder using self-referenced latent maximization plus reconstruction-guided color regularization, avoiding costly sample-wise online optimization. Across four T2I models and four datasets versus five baselines, it consistently degrades substitute-model generation quality while preserving visual fidelity.

arXiv cs.CR · 1d agoAI safety & security

DRIFT: Removing Diffusion Watermarks by Deflecting the Generative Trajectory

Introduces DRIFT, a black-box attack removing diffusion watermarks by deflecting generative trajectories, achieving 98-100% success across nine watermarking schemes.

Researchers propose DRIFT, a black-box watermark removal attack combining partial forward diffusion with stochastic reverse resampling to break trajectory-dependent verification. The paper derives information-theoretic and Wasserstein source-dependence bounds and shows the first verifier-rejected rung is least distorted among rejected rungs. Across nine watermarks spanning three paradigms, DRIFT achieves 98-100% attack success with the best image quality among compared attacks, without secret keys, verifier internals, or per-image gradient optimization.

arXiv cs.CR · 7d agoResearch

Large Universe Subset Predicate Encryption with IND-CCA Security (with Constant-size Ciphertext and Keys)

New construction achieves first large-universe subset predicate encryption with IND-CCA security and constant-size ciphertexts and keys under subgroup decision assumptions.

The paper proposes the first large-universe subset predicate encryption scheme achieving IND-CCA security with both constant-size ciphertexts and constant-size secret keys. Prior large-universe constructions by Chatterjee and Mukherjee either achieved only restricted selective security with constant sizes or adaptive security with attribute-dependent ciphertext size, and none achieved CCA security. The new construction is proven selectively secure under standard subgroup decision problems. Black-box transformations yield the first CCA-secure WIBE and WKD-IBE with constant-size ciphertexts and keys.

arXiv cs.CR · 1d agoResearch

What Makes Adversarial Examples Transfer Across Deepfake Detectors?

A controlled study of 60 deepfake detectors shows adversarial example transfer depends heavily on source-target compatibility, with source averaging understating vulnerability.

The study evaluates adversarial example transferability across 60 deepfake detectors spanning six backbones, two pretraining regimes, and five training-data configurations, using AutoAttack (AA) and Carlini-Wagner with Expectation over Transformation (CW-EOT). Transfer rises sharply when source and target share an exact backbone, architecture family, pretraining regime, or training data, with the dominant factor depending on the attack. Mean attack success rate is 7.21% under AA and 19.52% under CW-EOT for single sources, while a multi-source oracle reaches 64.48% after excluding exact matches, showing source averaging can substantially understate target vulnerability. The authors release 240,000 adversarially perturbed images, pairwise transfer results, detector configurations, and evaluation code.

arXiv cs.CR · 6d agoAI safety & security

Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

New framework tests whether LLM-cited explanation factors are necessary or sufficient, finding weak correlation across Claude, GPT, and Gemini models.

An arXiv paper introduces black-box intervention tests measuring whether factors LLMs cite in their explanations are necessary or sufficient for their outputs in agent oversight workflows. Across eight models from the Claude, GPT, and Gemini families, Spearman correlations between cited rankings and measured influence ranged from 0.349-0.354 (advisor recommendation) to 0.431-0.580 (prompt monitoring). Uncited factors scored above the lowest cited factor in up to 57.6% of advisor responses, showing cited top-three factors do not reliably identify the most influential inputs.

arXiv cs.AI / cs.LG / cs.CL · 11d agoAI safety & security

Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networks

ExCYDER framework self-verifies anomaly detection alerts for DER power grids using LightGBM and SHAP, reaching over 98% detection accuracy.

The paper presents ExCYDER, an explainable AI anomaly detection framework for Distributed Energy Resource networks that combines LightGBM with SHAP to validate whether each model decision aligns with its feature-attribution evidence. On a realistic DNP3 dataset it achieved over 98% detection accuracy, 14.5 ms SHAP latency per alert, and confidence deviation within 5%. The self-verifying mechanism distinguishes coherent from inconsistent alerts, improving interpretability and auditability for DER-focused security operations centers.

arXiv cs.CR · 4d agoResearch

Scalable Composition of Byzantine Agreements under Reorder Attacks

Researchers present the first adversary model combining party corruption with channel reordering attacks, establishing tight security thresholds for composed Byzantine agreement protocols.

The paper presents the first adversary model combining party corruption with adversarial channel attacks that reorder messages across multiple Byzantine agreement executions. It proves impossibility results for authenticated BA under parallel composition when n ≤ 3t or n ≤ 2c + 2t + 1, with matching possibility results when n > max{3t, 2c + 2t + 1}. The authors provide general black-box compilers plus erasure-correcting-code variants that achieve constant multiplicative communication overhead for long messages.

arXiv cs.CR · 6d agoResearch

New infosec products of the week: September 4, 2026

Weekly roundup covers F5's AI-powered WAF enhancements, Ping Identity's personal AI agent access, Superna 2.15 cyberstorage, and BugBase Pentest Copilot Enterprise.

This week's product roundup features releases from BugBase, F5 Networks, Ping Identity, and Superna. F5's WAF for Distributed Cloud adds anomaly detection and agentic threat intelligence for real-time virtual patching, Ping Identity launched Enterprise Personal Agent Access for securing personal AI agents, Superna 2.15 adds guided event-closing workflows for cyberstorage operations, and BugBase's Pentest Copilot Enterprise automates black-box pentesting across 100 vulnerability types using real Chromium browsers.

Help Net Security · 11d agoTools