BrokenHill

!\ Broken Hill logo \https://assets.kitploit.com/production/public/readmes/44608/c14154675c6f33f5c3887887afcdb39351c82f269ee2772cf31847106a20a871.jpg Broken Hill Broken Hill is a productionized, ready-to-use automated attack tool that generates crafted prompts to bypass restrictions in large...

CVE-2026-92894 Rubygem-foreman_ansible: unscoped lookupvalue deletion allows cross-model override value destruction

A flaw was found in the foremanansible plugin's Ansible override values API. The destroy action resolves the target LookupValue record by ID without verifying it belongs to an AnsibleVariable the caller is authorized to edit. An authenticated user with the editansiblevariables permission can dele...

CVE-2026-92894 Rubygem-foreman_ansible: unscoped lookupvalue deletion allows cross-model override value destruction

A flaw was found in the foremanansible plugin's Ansible override values API. The destroy action resolves the target LookupValue record by ID without verifying it belongs to an AnsibleVariable the caller is authorized to edit. An authenticated user with the editansiblevariables permission can dele...

CVE-2026-86761 snipe-it 8.6.3 before 8.7.0 Authorization Bypass via print endpoints

snipe-it versions before 8.7.0 contain an authorization bypass vulnerability in location print endpoints that fails to enforce per-model authorization checks. Authenticated attackers with location view permission can access printassigned and printallassigned endpoints to retrieve related users,...

Stealing AI Reasoning Traces

Interesting research: "Stealing Reasoning Traces from Proprietary LLM APIs": Abstract: Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces...

Security Vulnerability Patterns in AI-Generated Code: A Cross-Model Comparative Study

LLM-based coding tools enable non-expert users to generate routine automation scripts that may enter enterprise workflows without meaningful security review. This study examines that risk directly. Code was collected from ChatGPT, Microsoft Copilot, and Google Gemini using identical prompts acros...

Forensic Trajectory Signatures for Agent Memory Poisoning Detection

We discover a behavioral invariant in LLM agents under persistent memory poisoning: in architectures where routing information is retrieved through observable memory-tool invocations, successful attacks require calling memoryrecallfact before emailsendemail, a transition that non-exfiltrating...

Context-Based Adversarial Attacks on AI Code Generators: Vulnerability Analysis and Implications

AI-powered code generation systems have transformed software development but introduce critical inference-time security vulnerabilities. This research presents a systematic investigation of context-based adversarial attacks, where strategically crafted contextual inputs, including comments,...

How Reliable Are AI Attackers against a Fixed Vulnerable Target? A 400-Run Empirical Study of LLM Penetration Testing Consistency

Large language models LLMs can autonomously conduct multi-stage cyber attacks, but the consistency of their offensive behavior under repeated trials remains unstudied. This work presents the first large-scale empirical measurement of LLM attack consistency: 400 autonomous penetration testing runs...

The Surface You Test Is Not the Surface That Breaks

Tool-augmented LLM agents are vulnerable to prompt injection: a third party who controls part of the agent's context can plant instructions that the agent then executes as if they came from the user. Current evaluations report a single attack success rate per model on one channel, the tool output...

Refusal Before Decoding: Detecting and Exploiting Refusal Signals in Intermediate LLM Activations

In this paper, we investigate whether refusal behavior can be predicted from LLM intermediate activations before decoding using linear probes trained on residual stream activations at each transformer block. We find that refusal is linearly decodable well before the final layer, indicating that...

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models

While vision and multimodal foundation models underpin critical tasks from perception to complex reasoning, they remain highly vulnerable to adversarial attacks. However, traditional adversarial attacks are typically limited to single, predefined objectives, tightly coupling each attack to a...

Can You Keep a Secret? Involuntary Information Leakage in Language Model Writing

Language models are deployed in settings that require compartmentalization: system prompts should not be disclosed, chain-of-thought reasoning is hidden from users, and sensitive data passes through shared contexts. We test whether models can keep prompted information out of their writing. We giv...

VulKey: Automated Vulnerability Repair Guided by Domain-Specific Repair Patterns

The increasing prevalence of software vulnerabilities highlights the need for effective Automatic Vulnerability Repair AVR tools. While LLM-based approaches are promising, they struggle to incorporate structured security knowledge from sources like CWE and NVD. Current methods either use this...

Stop Fixating on Prompts: Reasoning Hijacking and Constraint Tightening for Red-Teaming LLM Agents

With the widespread application of LLM-based agents across various domains, their complexity has introduced new security threats. Existing red-team methods mostly rely on modifying user prompts, which lack adaptability to new data and may impact the agent's performance. To address the challenge,...

EUVD-2025-209209

Juju: Read All Controller Logs From Compromised Workload...

Incorrect Authorization

Overview Affected versions of this package are vulnerable to Incorrect Authorization debug log endpoint in the API server. An attacker can access sensitive log data belonging to any entity across any model by compromising a workload machine under the controller. Remediation A fix was pushed into...

CVE-2025-68152

CVE-2025-68152 affects Juju, an open source application orchestration engine. The issue permits a compromised workload machine under a Juju controller to read any log file for any entity in any model at any level, affecting versions 2.9 to before 2.9.56 and 3.6 to before 3.6.19. The flaw is mitig...

Not All Tokens Are Created Equal: Query-Efficient Jailbreak Fuzzing for LLMs

Large Language ModelsLLMs are widely deployed, yet are vulnerable to jailbreak prompts that elicit policy-violating outputs. Although prior studies have uncovered these risks, they typically treat all tokens as equally important during prompt mutation, overlooking the varying contributions of...

SUSE CVE-2026-1237

Vulnerable cross-model authorization in juju. If a charm's cross-model permissions are revoked or expire, a malicious user who is able to update database records can mint an invalid macaroon that is incorrectly validated by the juju controller, enabling a charm to maintain otherwise revoked or...