IOSSecuritySuite

⭐️ Do you want to become a certified iOS Application Security Engineer? ⭐️ Check out our practical & fully online course at: https://courses.securing.pl/courses/iase ISS Description by @r3ggi 🌏 iOS Security Suite is an advanced and easy-to-use platform security & anti-tampering library written in...

garak

garak, LLM vulnerability scanner Generative AI Red-teaming & Assessment Kit garak checks if an LLM can be made to fail in a way we don't want. garak probes for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses. If you know nm...

reasongate

ReasonGate A self-hostable gate that inspects the text going into and out of an LLM and returns an explainable allow / flag / block decision with a machine-readable audit record for every call. What this is The open-source core is rule-based. It does four things: recognizes known prompt-injection...

aco-prompt-shield

aco-prompt-shield 🛡️ Stop prompt injection attacks before they reach your LLM — zero API costs, runs entirely locally, integrates in 2 minutes. Prompt injection is the 1 security risk for LLM applications. aco-prompt-shield catches known jailbreak patterns, understands semantic intent via ML, and...

bordair-detector

Bordair Detector A two-stage detector for prompt injection and jailbreak attempts in LLM inputs. A regex gate settles the easy majority of traffic without touching the model; anything ambiguous falls through to a quantised DeBERTa-v3 classifier running in ONNX. The same engine covers image,...

Does the Unsafe Gradient Survive a Conversation? on the Fragility of Gradient-Based Jailbreak Detection in Multi-Turn Dialogue

Safety-aligned language models are commonly deployed as multi-turn assistants, which lets adversaries spread unsafe intent across several user turns instead of a single prompt. Gradient-based jailbreak detectors such as GradSafe were developed for single prompts: they score an input by the...

reasongate v0.4.0

ReasonGate A self-hostable gate that inspects the text going into and out of an LLM and returns an explainable allow / flag / block decision with a machine-readable audit record for every call. What this is The open-source core is rule-based. It does four things: recognizes known prompt-injection...

garak v0.17.0

garak, LLM vulnerability scanner Generative AI Red-teaming & Assessment Kit garak checks if an LLM can be made to fail in a way we don't want. garak probes for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses. If you know nm...

HiveTraceGuard-Pro: A Compact Generative Guardrail for Prompt Injection, Jailbreaks, and Adversarial Obfuscation

Production LLMs must handle inputs that attempt to override system instructions, bypass safety policies or elicit harmful responses. A common mitigation is a separate guardrail model. Existing reports, however, provide little evidence on Russian prompt injection or Russian surface obfuscation. We...

Measuring the Wrong Thing: Internal Harmfulness Scores Anti-Rank Successful Jailbreaks

Internal safety scores judge a prompt before any text is generated, and they are validated by how well they separate harmful prompts from benign ones. That separation is then read as evidence that the score will also catch the attacks that succeed. Harmful intent is a property of the prompt...

IOSSecuritySuite v2.3.0

⭐️ Do you want to become a certified iOS Application Security Engineer? ⭐️ Check out our practical & fully online course at: https://courses.securing.pl/courses/iase ISS Description by @r3ggi 🌏 iOS Security Suite is an advanced and easy-to-use platform security & anti-tampering library written in...

garak v0.16.0

garak, LLM vulnerability scanner Generative AI Red-teaming & Assessment Kit garak checks if an LLM can be made to fail in a way we don't want. garak probes for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses. If you know nm...

Re-Triggering Safeguards within LLMs for Jailbreak Detection

This paper proposes a jailbreaking prompt detection method for large language models LLMs to defend against jailbreak attacks. Although recent LLMs are equipped with built-in safeguards, it remains possible to craft jailbreaking prompts that bypass them. We argue that such jailbreaking prompts ar...

Tracing the Dynamics of Refusal: Exploiting Latent Refusal Trajectories for Robust Jailbreak Detection

Representation Engineering typically relies on static refusal vectors derived from terminal representations. We move beyond this paradigm, demonstrating that refusal is a dynamic and sparse process rather than a localized outcome. Using Causal Tracing, we uncover the Refusal Trajectory-a persiste...

EUVD-2019-13873

Malware in sbrugna...

ARMOR: Aligning Secure and Safe Large Language Models Via Meticulous Reasoning

Large Language Models LLMs have demonstrated remarkable generative capabilities. However, their susceptibility to misuse has raised significant safety concerns. While post-training safety alignment methods have been widely adopted, LLMs remain vulnerable to malicious instructions that can bypass...

Test-Time Immunization: a Universal Defense Framework against Jailbreaks for (Multimodal) Large Language Models

While multimodal large language models LLMs have attracted widespread attention due to their exceptional capabilities, they remain vulnerable to jailbreak attacks. Various defense methods are proposed to defend against jailbreak attacks, however, they are often tailored to specific types of...

Bypassing Prompt Injection and Jailbreak Detection in LLM Guardrails

Large Language Models LLMs guardrail systems are designed to protect against prompt injection and jailbreak attacks. However, they remain vulnerable to evasion techniques. We demonstrate two approaches for bypassing LLM prompt injection and jailbreak detection systems via traditional character...

CVE-2019-4266

IBM Maximo Anywhere 7.6.2.0, 7.6.2.1, 7.6.3.0, and 7.6.3.1 does not have device jailbreak detection which could result in an attacker gaining sensitive information about the device. IBM X-Force ID: 160199...

CVE-2019-4266

IBM Maximo Anywhere 7.6.2.0, 7.6.2.1, 7.6.3.0, and 7.6.3.1 does not have device jailbreak detection which could result in an attacker gaining sensitive information about the device. IBM X-Force ID: 160199...