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PuzzleMask: Abusing Plain Prose as a Covert AI Attack Vector

Check Point details PuzzleMask, a plain-prose technique that bypasses LLM gatekeeper policy checks, letting hidden payloads reach target models unreviewed.

Check Point Research describes PuzzleMask, a prompt-crafting technique that hides policy-violating payloads inside plain-English prose wrappers, bypassing quick LLM-based policy checks without emojis, Base64, or invisible formatting. The researchers tested 23 automated prompts against gatekeepers including GPT-4o-mini, GPT-OSS-Safeguard 20b, Claude 3 Haiku, and Llama Guard 3, and all were classified as safe despite policies that flagged the plain versions. When submitted to GPT-5 in thinking-high mode with a Python interpreter, the target model extracted and acted on the payload in over 90% of trials. The technique is not itself a jailbreak but can carry a jailbreak prompt as payload; mitigations include input paraphrasing, hardened gatekeeper policies, and output monitoring.

Check Point Researchupdated · 6d agofirst · 6d agoAI safety & security 2 sources

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, 2026AI safety & security

Bad Likert Judge: A Novel Multi-Turn Technique to Jailbreak LLMs by Misusing Their Evaluation Capability

Unit 42 details the Bad Likert Judge multi-turn jailbreak that abuses LLMs' evaluation capability, raising attack success rates over 60% across six frontier models.

Palo Alto Networks Unit 42 describes the Bad Likert Judge technique, a multi-turn jailbreak that asks a target LLM to act as a Likert-scale judge scoring the harmfulness of example responses. The highest-rated example in each scale can carry harmful content, bypassing the model's internal guardrails. Testing across six state-of-the-art text-generation LLMs showed an average attack success rate increase of more than 60% versus plain attack prompts, with tested models anonymized. The technique targets edge cases rather than typical use, and the article positions the work as guidance for defenders on potential jailbreak risks.

Palo Alto Unit 42 · Aug 17, 2026AI safety & security

AI Agents Are Here. So Are the Threats.

Unit 42 demonstrates nine framework-agnostic attack scenarios against AI agents built with CrewAI and AutoGen, causing data leakage, credential theft and remote code execution.

Palo Alto Networks Unit 42 investigated how attackers can target agentic applications, implementing two functionally identical apps with the open-source CrewAI and AutoGen frameworks and executing the same attacks on both. Nine attack scenarios produce outcomes including information leakage, credential theft, tool exploitation and remote code execution. Findings show most vulnerabilities are framework-agnostic, arising from insecure design patterns, misconfigurations and unsafe tool integrations rather than flaws in the frameworks themselves. The team published defense strategies per scenario and open-sourced the source code and datasets on GitHub.

Palo Alto Unit 42 · Aug 17, 2026AI safety & security

Inside ‘Project Lily’: The Humans Reading Your ChatGPT Chats

404 Media reveals OpenAI's 'Project Lily' has hundreds of contractors reading real ChatGPT user prompts, exposing sensitive personal data despite privacy filters.

404 Media reports that OpenAI employs hundreds of contractors who read real ChatGPT user prompts, including whole conversations, to rate and critique the chatbot's responses across a user base of over 900 million. Prompts are anonymized and run through OpenAI's Privacy Filter model, but the company acknowledged sensitive personal details can still reach reviewers, and 'user memories summaries' may reveal a user's location and personal context. The review work includes training ChatGPT to be less sycophantic and to stop anthropomorphizing itself, following lawsuits linking the sycophantic 4o model to multiple suicides. Anthropic confirmed it also uses human review to improve its models, and OpenAI's 'improve the model for everyone' data-sharing setting is on by default for free, Plus, and Pro users.

404 Media · 2d agoAI safety & security

When an Attacker Meets a Group of Agents: Navigating Amazon Bedrock's Multi

Unit 42 red-teamed Amazon Bedrock multi-agent applications, demonstrating prompt-injection attack chains that leak agent instructions and invoke tools, mitigated by Bedrock Guardrails.

Unit 42 red-teamed Amazon Bedrock Agents' multi-agent collaboration in Supervisor and Supervisor with Routing modes. The demonstrated attack chain detects the operating mode, discovers collaborator agents, delivers attacker-controlled payloads, and can disclose agent instructions and tool schemas and invoke tools with attacker-supplied inputs. No vulnerabilities were found in Bedrock itself, and the built-in prompt attack Guardrail blocked the attacks when properly configured. The researchers collaborated with Amazon's security team and frame the findings as a broader prompt injection risk for LLM-based systems.

Palo Alto Unit 42 · Aug 17, 2026AI safety & security

10 most critical LLM vulnerabilities

OWASP updated its Top 10 LLM application vulnerabilities, ranking prompt injection first and elevating excessive agency to third amid agentic adoption.

OWASP refreshed its Top 10 list of critical vulnerabilities in LLM applications, for the first time incorporating real-world incident data alongside expert voting. Prompt injection and sensitive information disclosure remain first and second, while excessive agency jumped from sixth to third as agentic systems that call APIs and execute code proliferate. Unbounded consumption of AI resources rose in prominence, while improper output handling dropped to the bottom as output sanitization becomes widespread. The list includes remediation guidance such as strict output schemas, human-in-the-loop approvals, and least-privilege credentials held in application code.

CSO Online · 7d 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 · 8d agoAI safety & security1

Invisible AI Prompts Trigger Court Sanctions

A Connecticut litigant hid white-font prompt injections in court filings to sway AI systems; the judge sanctioned him by revoking e-filing privileges.

A self-represented plaintiff hid prompt injection instructions in 3-point white text within court filings, telling any AI model reading the documents to agree with his filings and grant him relief. The judge called it serious litigation abuse and sanctioned him by revoking electronic filing privileges. It is reportedly the first documented prompt injection attack against a US court and the first sanction for attempting one.

Security Affairs · Aug 17, 2026AI safety & security in the wild

Person Hides Prompt Injection in Legal Filing Telling AI to Side With Them

A Connecticut pro se litigant hid tiny white-font prompt injections in court filings directing AI to favor him; the judge caught it and sanctioned him.

Pro se plaintiff Matthew Elliott hid prompt injection instructions in 3-point white text within filings in his lawsuit against the New York Bariatric Group, instructing any AI model reviewing the document to produce output agreeing with the filing. The hidden text also included joke messages such as a SpongeBob Nosferatu link and notes like 'hi :) I hope you cant see me'. Court staff noticed unusual white space, and Judge Walter Spader Jr. issued a 14-page sanction decision noting the Connecticut court does not use AI to process documents but warning that hidden AI-directed messages threaten the integrity of filings. Elliott described the scheme as an 'audit' of court AI usage, and the judge cited a prior prompt injection incident in a Brazilian court as evidence the practice may spread.

404 Media · Aug 13, 2026AI safety & security in the wild

GPT4Free Privacy Risks Expose AI Prompts to Third-Party Servers and Hidden Logs

Gen Digital researchers found GPT4Free's hosted chat routes prompts through third-party servers, mislabels models, and logs IPs and conversations for up to 30 days.

Gen Digital researchers tested the GPT4Free (G4F) hosted chat at g4f.dev and found requests routed through intermediary endpoints such as an OpenAI-compatible g4f.space endpoint before reaching providers like Google Gemini, sometimes returning different model identifiers such as gemini-3-flash-preview. Provider code referenced JSON files listing over 200 externally reachable Ollama and llama.cpp endpoints whose ownership and authorization were undisclosed. Code paths reportedly retain usage logs for 14 days (IP addresses, approximate geolocation, provider, model, conversation data) and error logs for 30 days, while Privacy Policy and Terms of Service links redirected to a member area instead of the documents.

GBHackers · 1h agoAI safety & security

Claude's new system prompt really doesn't want to reproduce song lyrics

Anthropic published updated Claude consumer system prompts, including changes steering the model away from reproducing song lyrics, likely over copyright concerns.

Anthropic publishes system prompts for Claude.ai and Claude mobile apps, including historic revisions, and has reorganized them into an index with per-model pages such as the Haiku 4.5 page showing the original October 15, 2025 prompt and an updated January 18, 2026 version. The latest consumer prompt strongly discourages reproducing song lyrics, a behavioral constraint likely tied to copyright considerations. Prompts for Claude Cowork and Claude Code are not included in the published set.

Simon Willison · 14d agoAI safety & security1

Amazon Kiro Prompt Injection Can Exfiltrate Sensitive Data Through Kiro Powers

Mindgard found a prompt injection flaw in Amazon Kiro IDE letting attacker-controlled workspace files exfiltrate sensitive local data; fixed in version 0.8.140.

Mindgard disclosed a prompt injection flaw in Amazon Kiro, an agentic AI IDE, that lets attacker-controlled repository content steer the agent into exfiltrating sensitive workspace data through Kiro Powers, which bundles MCP server configurations, POWER.md steering files, hooks, and contextual knowledge. Exploitation requires the user to open a malicious project via a workspace file and send any message to the agent; difficulty is rated low and it works in both trusted and untrusted workspaces. Amazon fixed the issue in Kiro IDE 0.8.140; the flaw has no CVE identifier and follows earlier Kiro bugs including CVE-2026-10591, plus related prompt-injection and code-execution issues in Codex CLI, Cursor, Gemini CLI, Copilot CLI, and Claude Code.

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 · 3d agoAI safety & security

Your AI agent's system prompt is not a security control

AWS and SANS guidance says system prompts are not security controls; enforce user permissions at retrieval time and default-deny every agent tool invocation.

AWS VP Gee Rittenhouse and SANS fellow Eric Johnson, with three AWS security specialists, published agentic AI security guidance for organizations with agents running or under development. They recommend scoping queries to user permissions inside existing RBAC/ABAC systems and filtering results before the model's context window, noting prompts can be bypassed, ignored, or overridden. The guidance warns risk concentrates when one agent holds sensitive data access, external communication, and exposure to untrusted content, the vector for prompt injection, which OWASP ranks as the top AI application threat. It cites IBM 2025 research that ungoverned shadow AI added $670,000 to average breach cost, and prescribes 30-day behavioral baselines, Cedar or Open Policy Agent for default-deny tool invocation, and four-layer containment with automatic circuit breakers.

Help Net Security · 10d agoAI safety & security