Search: “destructive-attacks”

Pick Your Poison: Learning to Select Poison Sets for Stronger LLM Backdoor Attacks

SAILS learns to select poison sets for LLM backdoor attacks, showing attack success ranges 3% to 80% at fixed poison counts across LLaMA-3-8B settings.

The paper shows existing backdoor evaluations that randomly sample a fixed number of poisoned examples severely underestimate worst-case vulnerability: across three LLaMA-3-8B settings, attack success ranges from 3% to 80% depending only on which poison set is chosen. SAILS formalizes poison selection as oracle-budgeted set optimization, learning a set scorer from a few hundred finetune-and-evaluate runs to rank millions of candidate sets and audit a small shortlist. It improves held-out attack success by 30 percentage points over the strongest influence baselines and transfers from small-scale to full-scale finetuning, extending to code-generation, agentic, and API-only backdoors.

arXiv cs.CR·Sep 14, 04:44 UTC · 2d agoAI safety & security 2 sources1

Do Input-Level Defenses Transfer to Observation-Level Attacks on VideoLLMs?

A systematic study shows input-level adversarial defenses provide inconsistent, often near-zero protection against observation-level attacks on video LLMs.

Researchers introduce DefTEval, a controlled framework testing eleven input-level defenses against five attack types across five video LLMs. Harmful-content detection rates are frequently near zero, and defenses fail even when attacks embed harmful signals in every sampled frame. Token compression discards localized safety features and modality fusion down-weights weakened visual signals, with defense outcomes dominated by model architecture rather than the defense method.

arXiv cs.CR·Sep 8, 06:59 UTC · 8d agoAI safety & security

InceptionRAG: Stealthy Poisoning Attack Against Retrieval-Augmented Generation

InceptionRAG fragments malicious payloads into dormant passages that trigger LLMs to self-deduce misinformation via multi-hop reasoning, bypassing existing RAG poisoning defenses.

Researchers introduce InceptionRAG, a stealthy corpus poisoning attack against retrieval-augmented generation that splits a malicious payload into a chain of individually harmless dormant passages. When retrieved together, the passages induce LLMs to self-deduce target misinformation through multi-hop reasoning, achieving over 80% attack success rate across three datasets and three LLMs under rigorous adversarial constraints. A zeroth-order suffix optimization (ZOSO) method automates authoritative suffix generation in black-box settings. The authors also propose HODOR, a document isolation defense that decouples adversarial logical dependencies.

arXiv cs.CR·Sep 15, 08:22 UTC · 1d agoResearch

Papercut AI Swarm Attack Heralds Changes for Cyber Kill Chain

Dark Reading reports AI-driven 'swarm' attacks like the PaperCut incident now span recon, lateral movement and exfiltration, forcing a rethink of the cyber kill chain.

Dark Reading examines how attackers are incorporating AI across the full kill chain, from building lab environments to stage and test agentic attacks through reconnaissance, lateral movement, and exfiltration. It cites a swarm-style AI attack on PaperCut systems as evidence that AI-enabled attackers are changing established defense and detection models.

Dark Reading·Sep 11, 15:48 UTC · 5d agoThreat actor

BdThemes Supply Chain Attack Poisons JSON to Create Rogue WordPress Admins

The Hacker News·Aug 11, 05:48 UTC · Aug 11, 2026VulnerabilityCVE-2026-18072CVE-2026-64638

Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety

Unit 42 research shows LLM safety refusals concentrate in a thin neural layer, motivating external, multi-layered AI security controls.

Palo Alto Networks Unit 42 introduces Perturbation Probing, a diagnostic technique for measuring the fragility of LLM safety mechanisms. The research finds that safety refusal behavior is localized within a thin neural layer, implying small perturbations can undermine built-in refusals. The authors argue this motivates external, multi-layered security defenses on top of model-internal safety training.

Palo Alto Unit 42·Aug 28, 22:00 UTC · 18d agoAI safety & security

Decoy Direction Optimization: A Post-Hoc Defense Against LLM Abliteration

Researchers introduce Decoy Direction Optimization, a cheap weight-editing defense that blinds refusal-direction ablation attacks against open-weight LLM safety guardrails.

Refusal Feature Ablation bypasses safety guardrails in open-weight LLMs by projecting out a linear refusal direction, often with high attack success rates. Decoy Direction Optimization injects a high-magnitude nonlinear decoy into MLP neurons so attackers' contrastive estimators ablate a harmless orthogonal feature instead. Evaluated across six model families, DDO keeps ASR below 10% under standard RFA and on Llama-3-8B-Instruct reduces Heretic weight-level attack ASR from 88.7% to 18%. It costs 30 to 450 times less per configuration than trained defense baselines.

Hugging Face daily papers·Sep 13, 20:00 UTC · 3d agoAI safety & security

Rethinking Indirect Prompt Injection as a Test-Time Search Problem

Researchers frame indirect prompt injection as test-time search, showing added attacker compute improves vulnerability discovery and exploitation against tool-using agents.

The paper models indirect prompt injection as a test-time search over a task-dependent attack surface shaped by the environment, user task, and injection goal. The authors build an agentic attacker with a dedicated search harness that performs reconnaissance, structured strategy reasoning, and adaptive evaluation using victim-agent feedback. Experiments show more attacker test-time compute improves discovery and exploitation of injection vulnerabilities, with explicit strategy management needed to avoid redundant search. The results argue that agentic security evaluations should characterize attacker search procedures and compute budgets rather than treating attack success as budget-independent.

arXiv cs.CR·Sep 3, 21:28 UTC · 13d agoAI safety & security

When the World Lies: Backdoor Attacks on Latent World Models for Downstream Control

A poisoned world-model checkpoint hijacks downstream controllers without an explicit trigger rule, passing clean-data evaluation while steering 100% of triggered actions.

Researchers show that a released pretrained world-model checkpoint acts as a supply-chain backdoor for downstream control. The poisoned model routes trigger-bearing observations into a chosen latent region and reshapes dynamics so the victim's own Dreamer-style actor training or MPC/CEM planning re-discovers attacker-targeted actions. The attack hijacks 100% of triggered steps in the strongest settings while retaining roughly 75% clean-task success and passing standard clean-data diagnostics. Moderate clean fine-tuning fails to remove the backdoor without substantially degrading clean control.

arXiv cs.CR·Sep 14, 16:01 UTC · 2d agoAI safety & security

The MAL Simulator: Cyber Operations Simulation based on Attack & Defense Graphs

MAL Simulator grounds attack-defense graph simulations in a CRATE-emulated network, training RL attacker and defender agents where attackers outperform search methods.

The MAL Simulator is a cyber operations simulator built on the Meta Attack Language (MAL), enabling decision-driven attack and defense simulations adaptable to new domains without modifying source code. Case studies trained defensive and offensive agents, grounded in data collected from an emulated network implemented in the CRATE cyber range. The trained attacker policy reached designated targets more efficiently than compared search methods, and the trained defender induced lower costs than a naive heuristic under noisy alerts, though defender performance dropped significantly against an RL attacker.

arXiv cs.CR·Sep 15, 02:59 UTC · 1d agoResearch

Not the Coyote, but the Road Runner: The Reality of Autonomous AI Attacks

Akamai argues autonomous AI attacks succeed through relentless, low-technique automation rather than novel super-weapons, which defeats traditional human-paced defenses.

Akamai's analysis contends that autonomous AI-driven attacks are not sophisticated new weapons but persistent, low-technique attacks that run continuously without human limits. The piece argues traditional defenses fail because they assume human-paced adversaries. It frames machine-speed, always-on attack cycles as the defining challenge for defenders.

Akamai Blog·Aug 26, 13:00 UTC · 21d agoAI safety & security

TCRF taken offline by DDoS attack after Claude user ban

The Cutting Room Floor game wiki was taken offline by a DDoS attack after a user leveraging Claude was banned.

The Cutting Room Floor (TCRF), a wiki documenting unused video game content, was knocked offline by a distributed denial-of-service attack. The attack reportedly followed moderation action banning a user who was using Anthropic's Claude. The incident highlights friction between community sites and AI-assisted users and tools.

Lobsters · security·Aug 28, 21:26 UTC · 19d agoAI safety & security in the wild

ToxicRAG: Compromising Retrieval-Augmented Generation Systems via Single-Shot Knowledge Poisoning Attacks

ToxicRAG shows a single narrative-form poisoned document can steer RAG answers, achieving 0.61-0.91 attack success rates across four LLMs.

The attack injects one document per target question written as a coherent knowledge-update narrative that acknowledges the previously accepted answer, introduces fabricated events that appear to invalidate it, and attributes the attacker-chosen answer to purported authorities. An optional answer-focused self-validation loop revises candidates when a surrogate LLM fails to reproduce the target answer. Across 100 target questions each from Natural Questions, HotpotQA, and MS-MARCO, with four victim LLMs and four dense retrievers, ToxicRAG achieves attack success rates of 0.61-0.91 and matches or exceeds the strongest baseline by 0 to 11 percentage points.

arXiv cs.CR·Sep 10, 04:42 UTC · 6d agoAI safety & security1

Deconstructing the Architecture of AI-Orchestrated Web Attacks

Akamai analyzes the architecture of AI-orchestrated web attacks, examining how AI-driven automation is reshaping offensive web operations.

Akamai published an analysis deconstructing the architecture of web attacks orchestrated with AI, based on the available title. The piece examines how AI-driven automation changes the structure of offensive web operations. No article text was provided, so specific findings are limited.

Akamai Blog·Aug 24, 06:00 UTC · 23d agoResearch

Attackers Exploit Critical Switchvox Flaw to Deploy Reverse Shells Without Credentials

Attackers exploit unauthenticated SQL injection CVE-2026-9586 in Sangoma Switchvox to run PostgreSQL commands and deploy reverse shells.

Threat actors are exploiting CVE-2026-9586 (CVSS 9.3), an unauthenticated SQL injection in Sangoma Switchvox SMB Edition 8.3 (104997), since August 30, 2026, running arbitrary SQL as the PostgreSQL superuser and achieving remote code execution. The /pa endpoint concatenates the user-controlled PhoneIP value into PostgreSQL queries; attackers can extract database contents, escalate to Switchvox web administrator, exfiltrate the cookie signing key to forge authentication, and invoke reverse shells. Sangoma patched the flaw in Switchvox 8.4.0.2 on July 14, 2026, roughly 4,000 instances are internet-exposed (mostly in the US), and honeypot activity from IP 176.65.148.184 deploys reverse shells followed by Base64-encoded process enumeration.

The Hacker News·Sep 2, 07:08 UTC · 14d agoExploit / PoC in the wildCVE-2026-9586

BragJack Attack Can Turn a Browser's Agentic AI Against It

New BragJack attack hijacks browsers' built-in agentic AI to access data, run malicious actions, and exfiltrate information.

Dark Reading describes BragJack, a new attack type that hijacks AI assistants built directly into web browsers. The technique allows attackers to access sensitive information, execute malicious actions through the agent, and exfiltrate data. The brief excerpt does not name affected browsers, CVEs, or confirm exploitation in the wild.

Dark Reading·16:43 UTC · 5h agoAI safety & security

Despite federal warnings, thousands of U.S. industrial controllers used in water systems remain exposed online

CyberScoop·Aug 6, 19:10 UTC · Aug 6, 2026Exploit / PoC in the wildCVE-2017-16740

Rust Supply Chain Attack Puts Build-Time Malware in Crates with 245 Million Downloads

Compromised maintainer account published malicious versions of popular Rust crates with 245M downloads, delivering a build-time payload via typosquatted proc-macro1 dependency.

The Rust Project deleted malicious releases of arrayref 0.3.10, internment 0.8.7, and append-only-vec 0.1.9 from crates.io after a compromised maintainer account added a typosquatted dependency, proc-macro1, whose build script downloaded and executed a remote payload during compilation. Each malicious version was online for 86 to 107 minutes on August 20, 2026, before deletion. The build script disabled TLS validation and deployed OS-specific implants on Unix, macOS, and Windows, with builds alone sufficient to trigger execution. Wiz Research confirmed active exploitation, noting the impacted packages appear in 35% of cloud and code environments and over 75% of Rust-using environments; arrayref alone has 245 million all-time downloads and 403 dependent crates.

The Hacker News·Aug 25, 16:13 UTC · 22d agoMalware in the wild1

Attack Chains, Not Just Attack Surfaces: Why Testing Individual Techniques Misses the Point

Filigran introduces Attack Chaining in OpenAEV to continuously simulate multi-stage attack paths, exposing gaps that isolated MITRE ATT&CK technique testing misses.

Filigran announced Attack Chaining, a new scenario type in its OpenAEV platform that links individual techniques into automated, continuously-run multi-stage attack paths, using each step's real output (credentials, tokens, open ports) to branch dynamically toward a final objective. The article cites Filigran's State of Threat Management report, in which 93% of security leaders reported a business-impacting cyberattack in the past 12 months, 88% said AI accelerates attackers, and 84% blamed siloed tools and disconnected testing. The 2025 DGFiP breach is cited as an example where individually survivable weaknesses chained into a major intrusion. The feature includes conditional chaining logic, live attack path mapping, structured findings for identifying chokepoints, and predefined scope and safety guardrails.

The Hacker News·Sep 15, 11:26 UTC · 1d agoTools

Frequently asked questions about the active threat to Siemens S7 Series PLCs

US government agencies warn that unattributed actors using AI-generated exploit scripts are targeting internet-exposed Siemens S7 PLCs in critical infrastructure.

A joint advisory from multiple U.S. government agencies warns that threat actors are exploiting known weaknesses and unnecessary internet exposure of Siemens S7 Series PLCs for reconnaissance and possible pre-positioning for future disruptive attacks. The attackers use AI to generate and refine exploit scripts faster than manual development would allow, lowering the technical bar for ICS attacks. There is no single patch, so defenders must reduce internet exposure and improve monitoring of ICS environments.

Tenable Blog·Aug 20, 14:01 UTC · 27d agoExploit / PoC in the wild

Finding Nemo(Claw): Networking Issue Allows for LLM Poisoning in OpenClaw

A networking flaw in Nvidia tooling lets attackers reach OpenClaw's local model server unauthenticated via the Ollama API, enabling persistent LLM poisoning.

Dark Reading reports that a networking issue in Nvidia's tooling can give attackers unauthenticated access to the local model server through the Ollama API. From there, attackers can poison the model used by the OpenClaw agent, creating persistent corruption of agent behavior. The finding highlights exposed local model servers as a security risk for self-hosted AI agent stacks.

Dark Reading·Aug 25, 19:50 UTC · 22d agoAI safety & security

TIER: Threat Implicitness Benchmark for Evaluating LLM Safety Behaviors

TIER benchmark shows LLM safety behaviors shift gradually across threat implicitness levels, with jailbreaks exposing the largest robustness gaps.

The TIER benchmark evaluates LLM safety behaviors across four risk domains and four threat levels, from explicit harmful requests to sophisticated jailbreaks, using a six-label behavior scale and two independent LLM judges. Experiments on six open-weight LLMs show safety behaviors evolve gradually across threat levels rather than flipping from refusal to compliance. Models with similar Attack Success Rates can exhibit distinct response distributions, arguing for behavior-aware safety evaluation.

arXiv cs.CR·Sep 4, 13:13 UTC · 12d agoAI safety & security

How Fragile Is Safety Alignment at Frontier Scale? A Single-Direction Attack on a 320B MoE

Researchers show directional ablation breaks refusal in GLM-5.3-Flash, a 320B-parameter MoE, cutting refusal by 41–89 points across seven benchmarks.

The study extends directional ablation, a white-box attack that removes an aligned LLM's refusal behavior, from dense models up to ~70B parameters to GLM-5.3-Flash, a 320B-parameter mixture-of-experts model with 288 routed experts, four-wide hyper-connection residual, and block-FP8 quantization. Editing attention, dense, and routed-expert writers jointly removes 0.776 of refusal, with 74% of the effect existing only under the joint intervention; the conventional module-name-based recipe reaches only 0.066 and fails silently on MoE architectures. The attack yields 41–89 percentage-point reductions in refusal across seven harmful benchmarks with no detected capability change, and a category-concentrated refusal residue survives all edits at ranks 1 to 12.

arXiv cs.CR·Sep 9, 06:48 UTC · 7d agoAI safety & security

Understanding the Security Boundary of Obfuscation-based On-Device LLM Protection

Researchers formalize obfuscation primitives for TEE-protected on-device LLMs and show a Collapse attack breaks ArrowCloak, TSQP, and LoRO, then extend the boundary.

The paper formalizes obfuscation primitives for TEE-Shielded LLM Partition (TSLP) schemes that offload computationally intensive layers from a Trusted Execution Environment to external GPUs. A novel primitive-guided attack, Collapse, demonstrates a shared vulnerability in prominent published methods including ArrowCloak (Security'25), TSQP (S&P'25), and LoRO (NeurIPS'25). The authors then introduce two new obfuscation primitives and integrate them with existing constructs to formulate an extended security boundary (O_ext).

arXiv cs.CR·Sep 9, 12:51 UTC · 7d agoAI safety & security

Hackers Abuse YouTube Gaming Channels and SEO Poisoning to Deploy RATs and Chrome Hijacker

Unit 42 exposed CL-CRI-1171, a pay-per-install operation using YouTube gaming channels and SEO poisoning to deliver RATs and a Chrome hijacker.

Palo Alto Networks Unit 42 identified over 10,000 OfferLoader samples and 11 YouTube channels pushing trojanized installers via gaming lures and SEO-poisoned searches for tools like WinDirStat and Bluetooth drivers. Trojanized Inno Setup installers spawn three payload branches: Insomnia RAT (dual Node.js/Python backdoor that disables Microsoft Defender), ARKTunnel (undocumented RAT hiding its payload in a bitmap image and tunneling TCP/UDP), and Docro Hijacker (alters Chrome protected preferences to hijack search and affiliate links). The pay-per-install model sells each infection to multiple buyers, reaching gamers as well as corporate and government endpoints.

Cyber Security News·Sep 14, 08:01 UTC · 2d agoMalware in the wild1

Agentic Ransomware: From Human-Operated to AI-Operated Attacks

SOCRadar analyzes the shift from human-operated ransomware to agentic AI-driven attacks and what this transition means for defenders.

The article traces ransomware's evolution from operations requiring human involvement, such as affiliates navigating networks by hand, toward AI-agent-operated attacks. It argues agentic ransomware could automate stages historically dependent on human operators. The piece discusses implications for detection and defensive planning.

SOCRadar·Sep 15, 09:59 UTC · 1d agoResearch

New Interrupt Injection Attack Can Bypass Spectre v2 Defenses on Intel and AMD CPUs

The Hacker News·Aug 8, 10:51 UTC · Aug 8, 2026VulnerabilityCVE-2023-20569

Security leaders must prepare for likely threats, not sensationalized agentic attacks

CSO opinion argues agentic AI attacks mostly exploit mundane vulnerabilities, urging defenders to train on realistic threat profiles rather than sensational containment breaches.

An opinion piece contends recent reports of AI models 'breaching containment' at OpenAI, Anthropic, and Meta overshadow the more likely risk: AI agents exploiting conventional unpatched flaws and insecure APIs. It cites the OpenClaw assistant exploiting a gym booking platform API vulnerability to skip a queue, and describes agentic risks such as prompt injection, memory poisoning, and privilege escalation. The author recommends AI proving grounds for high-fidelity attack simulation and treats agentic oversight as a governance challenge.

CSO Online·Sep 8, 09:00 UTC · 8d agoAI safety & security

The AI Attack Surface: How Threat Actors Abuse Trusted AI Platforms

Huntress explains how threat actors abuse trusted AI platforms as an attack surface for malware delivery and data theft.

Huntress's post describes threat actors targeting the AI attack surface, abusing trusted AI tools and platforms to deliver malware and steal data. Using legitimate AI services helps attacker activity blend into normal traffic and evade detection. The article frames AI platforms as an increasingly exploited part of the enterprise attack surface that defenders should monitor.

Huntress·Aug 27, 04:00 UTC · 20d agoAI safety & security in the wild