Search: “argument-injection”

Flextype v1.0.0-alpha.3 Stored Arbitrary Expression Injection in ExpressionsDirective Allows Arbitrary File Read

Flextype CMS v1.0.0-alpha.3 stores unsanitized expressions in entries, letting privileged authenticated users achieve arbitrary file read when fields are parsed.

Flextype CMS v1.0.0-alpha.3 contains a stored arbitrary expression injection flaw in the Entries ExpressionsDirective. An authenticated user with privileges to create or modify entries can persist arbitrary expression syntax in an entry field. When the field is later retrieved or processed, the stored value is passed to parsers()->expressions()->parse(), leading to arbitrary file read on the server.

Full Disclosure·Sep 4, 00:13 UTC · 12d agoVulnerability 8 sources

Cisco Integrated Management Controller Argument Injection Vulnerabilities

Cisco patched multiple argument-injection vulnerabilities in Cisco IMC's web management interface allowing authenticated attackers root command execution.

Cisco published an advisory covering multiple argument injection vulnerabilities in the web-based management interface of Cisco Integrated Management Controller (IMC). An authenticated, remote attacker could exploit them to execute arbitrary commands on the underlying operating system and elevate privileges to root. Cisco released software updates and states there are no workarounds; the advisory carries a High Security Impact Rating.

Cisco Security Advisories·Sep 15, 20:36 UTC · 13h agoAdvisory

[remote] mcp-server-kubernetes 3.8.x - Argument Injection

Argument injection in mcp-server-kubernetes 3.8.x MCP server could allow AI clients to execute unintended Kubernetes operations.

Exploit-DB entry 52638 documents an argument injection vulnerability in mcp-server-kubernetes version 3.8.x, a Model Context Protocol server that lets AI assistants interact with Kubernetes clusters. Crafted input can inject additional arguments into commands executed by the server, potentially enabling unauthorized operations or command execution against the cluster. This highlights growing security concerns around MCP servers bridging LLM agents and infrastructure.

Exploit-DB·Aug 11, 00:00 UTC · Aug 11, 2026Exploit / PoC

[0day-rubbish] core-admin 1.0.164 (build 16468) Systemic shell command injection via ineffective quote escaping (8.8)

0day Rubbish discloses a CVSS 8.8 shell command injection in core-admin 1.0.164 via ineffective quote escaping, enabling authenticated remote code execution.

0day Rubbish Research Team publicly disclosed a systemic shell command injection (CWE-78) in core-admin 1.0.164 (build 16468). The flaw stems from ineffective quote escaping and scores 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H). The disclosure does not mention a CVE identifier or observed exploitation in the wild.

Full Disclosure·Sep 8, 17:31 UTC · 7d agoVulnerability1

Corrupt Plans, Clean Traces: Evading Chain-of-Thought Monitoring with Plan Injection

Plan injection plants benign-sounding harmful reasoning that steers LLM actors to adversarial actions while evading chain-of-thought monitors.

Researchers show that injecting harmful but benign-sounding plans into an actor model's context causes it to perform adversarial actions while its reasoning passes chain-of-thought monitors, achieving 25-33% monitor evasion rates across benchmarks and scaling to larger models like DeepSeek-R1. Actor models paraphrase injected plans as their own reasoning without attribution. Giving the monitor access to the injected plan dropped detection by up to 50% on the Bio-Math task, with extra thinking tokens spent rationalizing rather than flagging the plan.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 18:33 UTC · 1d agoAI safety & security

ActGuard: Pre-execution Action Auditing against Indirect Prompt Injection in LLM Agents

ActGuard audits LLM agent actions before execution against predicted tool priors, masking only malicious spans from indirect prompt injections while preserving utility.

ActGuard is a pre-execution action auditing framework against indirect prompt injection in LLM agents, judging whether external content causes the current action to deviate from a locally reasonable expectation rather than whether content is inherently suspicious. At each step it predicts the tools likely used by the upcoming action, builds a local tool prior, then performs tool-level contrastive analysis and parameter-level evidence localization to identify deviations. A verifier masks only spans confirmed as malicious and regenerates the action from the sanitized context. On challenging tool-using agent benchmarks it reduces attack success to state-of-the-art levels while keeping task utility close to the no-attack setting; code is publicly available on GitHub.

arXiv cs.CR·Sep 14, 03:47 UTC · 2d agoAI safety & security

Cybercriminals Turn to Indirect Prompt Injection Attacks

Proofpoint reports cybercriminals are now adopting indirect prompt injection attacks against AI-powered systems.

Proofpoint's threat intelligence reports that cybercriminals have turned to indirect prompt injection attacks, extending the technique from a research concern into observed criminal tradecraft. The article is available by title only, so specific victims, campaigns, and targets are not detailed here.

Proofpoint Threat Insight·Aug 13, 13:25 UTC · Aug 13, 2026AI safety & security in the wild

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

ActionSplice: In-Flight Action Editing for Interactive World Models

ActionSplice enables in-flight action editing in chunk-autoregressive video world models via a lightweight corrector, avoiding rollback or waiting for the next chunk.

ActionSplice is an inference framework that formulates in-flight action editing for chunk-autoregressive video world models as Counterfactual State Transport (CST), where a lightweight corrector transports the interrupted backbone-native representation toward the matched state induced by the revised action. The world model and sampler remain frozen, and sampling resumes without replaying completed evaluations. Across minWM-Wan Action2V and HY-WM1.5, the retargeting variant CST-R reduces rollback-relative LPIPS by 61.5% and 75.9% versus direct condition swapping, while the temporal-splicing variant CST-T reduces suffix LPIPS by 56.1% and 77.5% with 2.73x and 1.69x pixel-ready speedups over waiting.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research

Hiding Prompt Injection in Legal Filing

A judge banned a plaintiff from electronic court filings after hidden prompt-injection text was discovered planted in legal documents.

Bruce Schneier's blog discusses an incident in which hidden prompt-injection instructions were planted inside a legal filing, apparently targeting AI systems that might process court documents. Judge Walter Spader Jr. responded by banning the plaintiff from electronic filings, requiring all future submissions as printed hard copies. Commenters debate whether the tactic could affect future AI-based processing of court records and whether plain-text formats will regain favor.

Schneier on Security·Aug 31, 11:04 UTC · 15d agoAI safety & security in the wild

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·Sep 3, 22:44 UTC · 12d agoAI safety & security

PIDS-Bench: Evaluating Prompt-Injection Detectors Under Over-Defense, Obfuscation, and Distribution Shift

PIDS-Bench shows prompt-injection detectors scoring F1 above 0.98 still misclassify about one-third of external benign security-adjacent prompts, revealing provenance-sensitive over-defense.

PIDS-Bench is a frozen multi-axis benchmark that jointly evaluates prompt-injection detectors on attack detection and benign false-positive behavior at fixed thresholds, spanning in-distribution inputs, hard-benign prompts, obfuscated attacks, and domain/structural distribution shifts. It evaluates seven detectors plus a rule-based lower-bound reference. A detector exceeding F1 = 0.98 on held-out data still misclassifies roughly one-third of an externally-sourced benign security-adjacent subset, and no internal detector reaches F1 >= 0.95 with hard-benign FPR <= 0.10 on the stress distribution. Hard-negative augmentation nearly eliminates over-defense on curated stress inputs but leaves it intact on externally-sourced prompts, a pattern termed provenance-sensitive over-defense.

arXiv cs.CR·Sep 14, 04:28 UTC · 2d agoAI safety & security

[0day-rubbish] QuantaStor 6.8.3.018 Command injection in the alert-mail command via the smtpPassword field (8.8)

QuantaStor 6.8.3.018 has a CVSS 8.8 command injection in its alert-mail command, exploitable via the smtpPassword field.

0day Rubbish Research Team disclosed a command injection (CWE-78) in QuantaStor 6.8.3.018's alert-mail command, reachable through the smtpPassword field. The flaw scores CVSS 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H). No CVE identifier or evidence of active exploitation is mentioned in the disclosure.

Full Disclosure·Sep 8, 17:31 UTC · 7d agoVulnerability 2 sources

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 · 12d agoAI safety & security

When the prompt becomes the payload: A practical pen-testing guide for GenAI, LLM and RAG applications

CSO Online publishes a practical penetration-testing guide for GenAI, LLM, and RAG applications, covering prompt injection, retrieval poisoning, and tenant isolation testing.

The guide frames LLM applications as attack graphs spanning prompts, retrieval layers, vector stores, tools, identities, and downstream APIs, arguing that conventional web testing misses instruction-vs-data channel risks. It builds on OWASP prompt injection guidance (direct vs. indirect injection) and NIST's 2025 adversarial machine-learning taxonomy, noting that RAG and fine-tuning do not remove injection risk. Recommended practices include documenting trust transitions across components, using canaries and synthetic records to avoid test side effects, running multi-turn and obfuscated injection campaigns, and verifying chains from poisoned documents to observable state changes. It also details testing RAG pipelines via controlled document poisoning across metadata, OCR layers, and code comments, plus cross-tenant isolation checks on retrieved document IDs.

CSO Online·Sep 9, 10:00 UTC · 7d agoAI safety & security1

The Hidden Instructions That Can Hijack AI Agents

Hidden prompt injections embedded in documents and metadata can hijack autonomous AI agents, causing data exfiltration and out-of-policy actions at machine speed.

Bowbridge warns that hidden indirect prompt injections, embedded in documents, metadata, emails, images, and code repositories, can cause autonomous AI agents to treat attacker-controlled content as trusted guidance. Because agents inherit user privileges, act silently, and lack human judgment, injections can lead to data exfiltration or file poisoning that traditional security controls cannot detect. A real-world example involved a supplier quote whose metadata instructed an agent to override guidance and select the most expensive option. Bowbridge recommends scanning documents before agents process them.

SecurityWeek·Sep 8, 17:00 UTC · 7d agoAI safety & security

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

Researcher shows how Claude Code can be tricked simply by asking it to summarize a website

Researcher wunderwuzzi showed Claude Code can be hijacked via prompt injection simply by asking it to summarize a website.

Security researcher Johann Rehberger (wunderwuzzi) demonstrated that Claude Code can be manipulated through prompt injection by simply asking it to summarize a website. Instructions embedded in fetched web content are executed by the agent, hijacking its behavior. The Register frames the finding as another demonstration of prompt injection risks in agentic coding tools that ingest untrusted web content.

The Register · Security·Aug 28, 20:50 UTC · 18d agoAI safety & security1

[vim-security] Ex Command Injection in sign_jump() in Vim < v9.2.1090

Vim sign_jump() before v9.2.1090 permits Ex command injection via unescaped buffer names; low-severity patch disclosed by Christian Brabandt.

Christian Brabandt disclosed an Ex command injection vulnerability in Vim's sign_jump() function affecting versions before v9.2.1090, caused by improper neutralization of unescaped buffer names. The issue is rated Low severity and maps to CWE-88 (argument injection) and CWE-94 (code injection). A CVE has been requested but not yet assigned.

oss-security·Sep 12, 17:53 UTC · 3d agoVulnerability

[webapps] EasyAppointments 1.5.1 - Blind SQL Injection

A proof-of-concept exploit for a blind SQL injection vulnerability in EasyAppointments 1.5.1 has been published on Exploit-DB.

Exploit-DB lists a public proof-of-concept exploit for a blind SQL injection flaw in EasyAppointments 1.5.1. The listing falls under the webapps category and enables reproduction of the injection. No in-the-wild exploitation or CVE identifier is stated in the listing.

Exploit-DB·Sep 1, 00:00 UTC · 15d agoExploit / PoC1

[webapps] CorgetGpsDget 2_3.2 - OS Command Injection

Exploit-DB publishes OS command injection exploit for the obscure CorgetGpsDget 2_3.2 web application.

A public exploit demonstrates OS command injection in CorgetGpsDget version 2_3.2. The flaw could allow attackers to execute arbitrary operating system commands on the hosting server. The product appears to have limited deployment, reducing real-world exposure.

Exploit-DB·Aug 10, 00:00 UTC · Aug 10, 2026Exploit / PoC

Prompt Injection Remains Biggest LLM Risk, Despite Limited Incidents

Infosecurity Magazine·Aug 5, 11:00 UTC · Aug 5, 2026AI safety & security

When AI Remembers Too Much

Unit 42 PoC shows indirect prompt injection can poison Amazon Bedrock Agent long-term memory, enabling silent exfiltration of conversation history across future sessions.

Palo Alto Networks Unit 42 published a proof of concept showing that indirect prompt injection can silently poison the long-term memory of Amazon Bedrock Agents when the memory feature is enabled. Malicious content on a webpage or document manipulates the agent's session summarization process, so injected instructions persist across sessions and are added to later orchestration prompts, silently exfiltrating user conversation history. The issue is not a vulnerability in the Amazon Bedrock platform but an illustration of the broader unsolved LLM prompt-injection challenge. Amazon reviewed the research and stated that Bedrock Guardrails with the prompt-attack policy provides effective mitigation.

Palo Alto Unit 42·Aug 17, 12:32 UTC · 29d agoAI safety & security

On Identifying Adversarial Intent Injection in AI-Native 6G Networks

Dual-path CNN and AutoEncoder framework detects adversarial intent injection in AI-native 6G networks, reaching 0.97 accuracy and 0.98 F1.

The paper defines a fine-grained threat model for adversarial intent injection in AI-native 6G intent-based networking, where malicious policies are disguised within benign intent flows. It evaluates four injection strategies: stealth-mode, random distribution, increasing frequency, and decreasing frequency. A dual-path detection framework combines a CNN using TF-IDF features for supervised detection with an AutoEncoder trained only on benign data for one-class detection, reaching 0.97 accuracy and 0.98 F1-score, roughly 9% and 36% gains over the state-of-the-art baseline.

arXiv cs.CR·Sep 10, 19:23 UTC · 5d agoResearch

[remote] D-Link DNS_340L - OS Command Injection

An OS command injection exploit was published for the D-Link DNS-340L NAS, a flaw relevant to exposed network storage devices.

Exploit-DB published exploit #52643 for the D-Link DNS-340L network-attached storage device. The vulnerability is an OS command injection, which could allow arbitrary command execution on affected devices. The disclosure text does not report exploitation in the wild.

Exploit-DB·Aug 17, 00:00 UTC · Aug 17, 2026Exploit / PoC

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

Misleading the Planner through Deceptive Resumes: Registration-Time Injection in Centralized Multi-Agent Systems

Researchers demonstrate registration-time prompt injection in centralized LLM multi-agent systems, dropping GAIA task success from 84.31% to 37.25%, and propose DescGuard defense.

The paper identifies a registration-time injection channel in centralized LLM multi-agent systems where third-party worker agent descriptions are trusted by the planner before any user instruction arrives. Analyzing 32,000 descriptions from three public agent marketplaces, at least 23.35% contain content outside the four defined description fields. Eight description-manipulation attack strategies targeting task decomposition, capability grounding, and subtask specification cut GAIA task success from 84.31% to 37.25% and increased token consumption or execution time by over 111%, persisting across two MAS implementations, six planner LLMs, and four evaluators. The proposed DescGuard defense filters descriptions to worker-scoped interface information and restores metrics toward baseline without modifying workers, planner, or orchestration logic.

arXiv cs.CR·Sep 14, 13:06 UTC · 1d agoAI safety & security

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 sources

BenchShield: Formal Model-Backed Instrumentation for Reward Integrity in LLM-Agent Evaluation Infrastructure

BenchShield uses lifecycle-model-backed instrumentation to detect reward hacking in LLM-agent benchmarks, lifting full-chain recall to 77-100% at up to 65% lower cost.

The framework grounds reward-hacking detection in a finite lifecycle model of an evaluation's reward-relevant events, combining a static phase-aware taint analysis with runtime infrastructure-side evidence attribution. Evaluation used a human-labeled corpus of 456 adjudicated trajectories drawn from more than 31,000 public agent runs across three benchmarks. BenchShield improves full-chain recall from 23-94% to 77-100% and same-vector coverage from 16-56% to 43-78%, cuts per-task cost by up to 65%, and achieves 96% accuracy detecting reward hacking at runtime.

arXiv cs.CR·Sep 10, 03:10 UTC · 6d agoAI safety & security1

O-CMS 1.0.0 Authenticated OS Command Injection via ai_cli_script

O-CMS 1.0.0 has an authenticated OS command injection in the ai_cli_script parameter, letting privileged attackers execute arbitrary commands.

O-CMS version 1.0.0 contains an authenticated OS command injection vulnerability in its AI CLI configuration functionality. An authenticated attacker with sufficient privileges can supply shell metacharacters and extra commands through the ai_cli_script parameter of /admin/settings/save. The attacker-controlled CLI value is then executed when the configured AI provider is tested via /admin/settings/test-ai.

Full Disclosure·Sep 4, 00:13 UTC · 12d agoVulnerability1